International journal of automotive technology, tập 10, số 5, 2009

International Journal of Automotive Technology, Vol 10, No 5, pp 529−535 (2009) DOI 10.1007/s12239−009−0061−x Copyright © 2009 KSAE 1229−9138/2009/048−01 EFFECT OF DIRECT IN-CYLINDER CO INJECTION ON HCCI COMBUSTION AND EMISSION CHARACTERISTICS * S QU, K DENG , L SHI and Y CUI Key Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China (Received 31 December 2007; Revised 24 October 2008) ABSTRACT−Fuel injection during negative valve overlap period was used to realize diesel homogeneous charge compression ignition (HCCI) combustion In order to control the combustion, CO2 in-cylinder injection was used to simulate external EGR Effects of CO2 injection parameters (injection timing, quantity, pressure) on HCCI combustion and emission characteristics were investigated Experimental results revealed that CO2 in-cylinder injection can control the start of combustion and effectively reduce NOx emission Either advancing CO2 injection timing or increasing CO2 injection quantity can reduce peak cylinder pressure and mean gas temperature, delay the starts of low temperature reaction (LTR) and high temperature reaction (HTR), and lower pressure rise rate; NOx emission was reduced, while smoke, HC, and CO emissions increased Since the combustion phase was improved, the indicated thermal efficiency was also improved Injection pressure determines the amount of disturbance introduced into the cylinder Generally, with the same injection quantity, higher injection pressure results in higher momentum flux and total momentum Larger momentum flux and momentum has a stronger disturbance to air-fuel mixture, resulting in a more homogeneous mixture; therefore, larger injection pressure leads to lower NOx and smoke emissions KEY WORDS : Homogeneous charge compression ignition, Gas injection system, Injection timing, Injection quantity after the cylinder pressure reached its peak The air jet stirred the stagnant flame and promoted soot oxidation The results showed that the air-cell system effectively reduced soot emission at medium and high loads Nagano et al (1991) added a plunger and a spring in the air cell When the air was pushed into the air cell, it also pushed the plunger, and the spring was compressed so that the momentum flux of the air increased when air was injected into the main chamber Several studies were made by Kurtz et al and Choi et al (Choi and Foster, 1995; Kurtz et al., 1998, 2000) by using auxiliary gas injection to increase incylinder mixing during the latter portion of combustion to reduce soot emission Effects of gas injection direction, compositions, momentum, and injection tim-zing on reducing soot emission were studied New combustion concepts have also been developed, such as HCCI, which can simultaneously reduce NOx and smoke emission HCCI combustion is intensively studied due to its emission reduction potential for DI diesel engines Unlike conventional diesel combustion, air and fuel of HCCI combustion are premixed, and the homogeneous mixture is auto-ignited at multiple points throughout the cylinder NOx emission is dramatically reduced due to the low combustion temperature resulted from a lean air-fuel mixture, and PM emission is reduced due to a well premixed mixture and absence of fuel rich zones However, HCCI combustion can only be realized in low and medium INTRODUCTION Diesel engines are widely used in trucks and buses due to their superior fuel economy, high torque at low speed, durability, and reliability, and are increasingly used as car engines However, conventional diesel combustion has the problem of high NOx and particulate matters (PM) emissions, and a trade-off relationship exists between NOx and PM emissions According to a recent simulation study (Kitamura et al., 2002) on n-heptane (a simulation substitute for diesel), NOx is formed in the high temperature region (T > 2200 K) and oxygen rich zones, while PM is formed in a certain temperature range (1500 K~2500 K) and fuel rich zones (φ > 2) A technique that can reduce one type of emissions would increase the other type of emissions For example, EGR can effectively reduce NOx emission, but it would increase PM emission With increasingly stringent emission regulations, many techniques have been used to reduce diesel engine emission, one of which was to use air jet to promote air-fuel mixing during the diffusion combustion phase to reduce soot emission Kamimoto et al (1983) added an air cell in the cylinder head of a direct injection (DI) diesel engine He found that air was pushed into the air cell during the compression stroke and was injected into the main chamber *Corresponding author e-mail: kydeng@sjtu.edu.cn 529 530 S QU, K DENG, L SHI and Y CUI engine operating regimes because it is hard to control the combustion rate at high load In studies in the last few years, internal and external EGR were widely used to control HCCI combustion The former is mainly used in gasoline-fueled HCCI engines to dilute the homogeneous mixture in order to obtain sufficiently high temperatures for auto-ignition (Onishi , 1979) The latter is mainly used to control diesel-fueled HCCI combustion (Peng , 2003; Okude , 2004; Hardy , 2006; Kimura , 2001) External EGR can reduce in-cylinder oxygen concentration and cylinder temperature due to its high heat capacity; as a result, NOx emission can be reduced In addition, ignition timing is delayed so that fuel-air mixing time is increased and local fuel-rich zones are reduced; hence, soot emission can be reduced However, external EGR also has some disadvantages For example, EGR quantity is hard to control in transient mode; it cannot promote in-cylinder mixing; finally, it takes part of the intake air volume and affects air charge efficiency In this paper, the authors used fuel injection during the negative valve overlap period to realize diesel HCCI combustion (Shi , 2005, 2007) In order to control the combustion, CO2 was used to replace external EGR and was directly injected into the engine cylinder by using an air injection system Benefits of using CO2 in-cylinder injection are as follows: firstly, CO2 injection quantity can be adjusted to satisfy the EGR requirement; secondly, by adjusting CO2 injection parameters (injection timing, pressure, quantity), the CO2 jet adds extra momentum in the cylinder; finally, the air charge efficiency is not affected if the injection timing occurs after intake valve close timing (IVC) In this paper, the effects of CO2 injection parameters on HCCI combustion and emission characteristics were investigated et al et al et al et al et al Table Test engine specifications Item Specification Engine type 4-valve, DI, single cylinder Bore×Stroke 135 mm × 150 mm Displacement vol 2.15 L Compression ratio 14.8 Combustion chamber ω type Fuel injection system Common Rail Injection System Fuel injector nozzle 7×0.2 mm (Radial holes) Spray Angle 150o+Axial hole) Valve-train VVT EVC = 345oCA ATDC IVO = −345oCA ATDC et al EXPERIMENTAL APPARATUS AND PROCEDURE 2.1 Test Engine The test engine is a four-valve, single cylinder, naturally aspirated DI diesel engine, and its specifications are shown in Table The fuel injection system is DENSO ECD-U2 common rail injection system The valve train is a Variable Valve Timing (VVT) system In the experiments, the VVT system was adjusted so that the engine had a valve overlap of −30oCA, and a gas injection system was used to achieve CO2 injection In the experiments, AVL DiGAS 4000 light was used to measure NOx by using an electrochemistry method and HC and CO emissions by the nondispersive infrared (NDIR) method Total HC emissions cannot be accurately measured by NDIR method; however, the HC emission trend should be correct AVL 439 Opacimeter was used to measure smoke opacity 2.2 Gas Injection System Figure Cross section view of combustion chamber A gas injection system was used to inject CO2 into the cylinder It is composed of a high-resolution electronic scale (precision of g, range of 0~100 kg), a gas bottle, a pressure regulator, and a gas injector and its drive box The solenoid activated gas injector, manufactured by Guizhou Honglin Ltd, was installed in the cylinder head It comprises a solenoid valve and an injector body A check valve was installed in the injector body to prevent reverse flow of cylinder gas The nozzle tip of the gas injector has a single hole, mm in diameter, oriented 23o downward into the combustion chamber, as shown in Figure The high-resolution electronic scale was used to measure the mass reduction of the gas bottle in three minutes and then calculate cyclic gas injection quantity Detailed information about the gas injection system and gas injection calibration can be obtained from a study by Qu (Qu , 2008) et al 2.3 Experimental Procedure In this paper, premixed diesel-air mixture was prepared by injecting diesel early during the negative valve overlap period (see Figure 2) This early injected diesel can fully utilize the heat of the internal EGR and piston to evaporate Furthermore, the wall-wetting caused by over-penetration of fuel can be avoided At the same time, to control HCCI combustion, CO2 was injected directly into the cylinder The fuel injection timing was studied first, followed by the EFFECT OF DIRECT IN-CYLINDER CO2 INJECTION ON HCCI COMBUSTION 531 Figure Fuel injection and CO2 injection mode Figure Effects of fuel injection timing on HCCI emission Figure Effects of fuel injection timing on HCCI combustion effects of CO2 injection parameters, including injection timing, injection quantity, and injection pressure The engine was run at 1300 r/min in all experiments EXPERIMENTAL RESULTS AND ANALYSIS 3.1 Effect of Fuel Injection Timing The effects of fuel injection timing on HCCI combustion and emission characteristics for engine speed of 1300 r/min and cyclic fuel injection quantity 0.057 g are shown in Figure and Figure 4, respectively Figure indicates that fuel injection timing (IT) has a large effect on cylinder pressure and heat release rate (HRR) As the fuel injection timing is delayed, maximum cylinder pressure and maximum heat release rate decrease Figure reveals that, as the injection timing is delayed, NOx decreases, HC and CO increase, and smoke opacity remains unchanged In addition, HC and CO emissions not significantly change before injection timing of −365oCA but rapidly increase afterwards This can be explained by the fact that late injected fuel cannot sufficiently use the heat of internal EGR, which decreases the vaporization ratio of fuel at ignition timing and reduces combustion efficiency The choice of fuel injection timing during negative valve overlap must satisfy two criteria First, fuel injection timing should advance to make use of heat of internal EGR Second, injection timing should not be too early in order to avoid early ignition and excessively rapid combustion rate In the following experiments, fuel injection timing was fixed at −365oCA ATDC Since the amount of NOx emission indicates the incylinder combustion condition, the authors propose to use NOx emission as an indicator of upper load limit When the load increases to a certain level, auto-ignition occurs too early, such that the rapid heat release results in a high pressure rise rate, high peak cylinder pressure, and, hence, high mean gas temperature The resulted high temperature leads to a high NOx formation rate and high NOx emission In this study, the indicated mean effective pressure (IMEP) at which NOx emission exceeded 100 ppm was taken as the upper load limit For engine speed of 1300 r/min and fuel injection timing at −365oCA ATDC, the upper load limit is IMEP = 4.5 bar 3.2 Effect of CO2 Injection Timing on HCCI To investigate CO2 injection timing on HCCI combustion, several cases in addition to the baseline case (without CO2 injection, n = 1300 r/min, cyclic fuel injection quantity 0.062 g, IMEP = 4.7 bar) were run At these cases, CO2 injection pressure was MPa, and gas injection pulse width was ms Because the gas injection pressure was high while cylinder pressure was low during the injection process, jet flow out of the gas injector remained choked throughout the injection process Therefore, the cyclic gas injection quantity remained at a constant value, 0.092 g The gas injection timing varied every 40°CA between −320oCA ATDC and −80°CA ATDC (Figure 2) CO2 injection timing determines its concentration distribution at the end of a compression stroke An early injecting timing leads to a long mixing time with cylinder air and a relatively even CO2 concentration distribution; in contrast, late injection timing leads to uneven distribution with locally rich or poor CO2 concentration zones; hence, 532 S QU, K DENG, L SHI and Y CUI Figure Effects of CO2 injection timing on HCCI emissions Figure Effects of CO2 injection timing on HCCI combustion heterogeneity is large Different CO2 distribution causes different temperature distribution and affects local fuel vaporization Figure shows the effects of CO2 injection timing on cylinder pressure, mean gas temperature (T), pressure rise rate (dp/dϕ), and heat release rate With an advance of CO2 injection timing, peak pressure decreases, and its corresponding crank angle is delayed; mean gas temperature decreases; both low and high temperature reactions are delayed; pressure rise rate slightly decreases This can be explained by the fact that as CO2 injection timing advances, its concentration distribution becomes more even; hence, it occupies a larger part of cylinder volume so that gas temperature and pressure rise in these areas are reduced due to the high heat capacity of CO2 Since HCCI ignition is controlled by chemical kinetics, once the intake valve closes and pressure-temperature-composition history during the compression stroke determines the start of low temperature heat release, advancing CO2 injection timing can delay the start of low temperature reaction and, hence, the start of high temperature reaction In addition, a decrease in local oxygen concentration reduces combustion rate, resulting in low peak cylinder pressure and maximum pressure rise rate A decrease of mean gas temperature is not only related to lowered combustion rate and the high heat capacity of CO2, but also, to a certain extent, related to the low temperature of injected CO2 Figure illustrates the effects of CO2 injection timing on HCCI emission It can be seen that CO2 in-cylinder injection can effectively reduce NOx emission With an advance of CO2 injection timing, NOx rapidly decreases until −160 o CA; at this injection timing, NOx is below 100 ppm NOx reduces with further advancing injection timing and reaches its lowest level at −240oCA ATDC, reduced by 91% relative to the baseline case Two reasons are considered to be responsible for the reduction of NOx One is the reduction of mean gas temperature The other is that, if CO2 is injected before IVC, average in-cylinder oxygen concentration decreases Both reasons are beneficial for restraining NOx formation Smoke opacity slightly increases with CO2 injection due to the reduction of cylinder temperature, which is detrimental to soot oxidation HC and CO also slightly increase due to the decrease of temperature and oxygen concentration, which are detrimental to the oxidation reaction 3.3 Effect of CO2 Injection Quantity on HCCI Figure shows the effects of CO2 cyclic injection quantity on cylinder pressure and heat release rate at CO2 injection timing of −240oCA ATDC With the increase of CO2 cyclic injection quantity, peak cylinder pressure decreases, and its corresponding crank angle is delayed; the starts of both LTR and HTR are delayed This is because the high heat capacity of CO2 is beneficial to lower temperature at the end of a compression stroke, and, hence, ignition delay is prolonged Figure illustrates the effects of CO2 injection quantity on combustion characteristics With the increase of CO2 cyclic injection quantity, peak mean gas temperature (Tmax) and peak pressure rise rate ((dp/dϕ)max) decrease, and SOC is delayed Compared to the baseline case, SOC is delayed EFFECT OF DIRECT IN-CYLINDER CO2 INJECTION ON HCCI COMBUSTION Figure Effects of CO2 cyclic injection quantity on cylinder pressure and heat release rate Figure Effects of CO2 cyclic injection quantity on HCCI combustion characteristics by 8oCA with CO2 cyclic injection quantity of 0.14 g Since combustion phase delay effectively reduces compression work, indicated thermal efficiency (η it) slightly increases Figure shows the effects of CO2 injection quantity on emission levels It can be seen that increasing CO2 injection quantity can dramatically reduce NOx emission With an injection quantity of 0.14 g, NOx emission is reduced by 97%, from baseline’s 600 ppm to 19 ppm Lowered oxygen concentration and combustion temperature are considered to be responsible for this Soot emission depends on the competition between its formation rate and oxidization rate Lowered oxygen concentration tends to increase soot formation rate; increased heat capacity and reduced temperature tend to hinder soot oxidization rate As a result, smoke opacity increases with the increase of CO2 injection quantity HC and CO have a similar tendency 3.4 Effect of CO2 Injection Pressure on HCCI To investigate the effect of CO2 injection pressure on HCCI combustion, three injection pressures (1 MPa, MPa, 533 Figure Effects of CO2 cyclic injection quantity on emissions Figure 10 Effects of CO2 injection pressure on emissions MPa) were considered In all three cases, CO2 cyclic injection quantity was maintained at 0.092 g by adjusting injection pulse width Figure 10 shows the effect of injection pressure on emissions It can be seen that higher injection pressure results in lower NOx and smoke opacity at most injection timings, while HC and CO rarely vary with pressure change To explain these results, two assumptions are made First, we assume that gas flow through the pressure regulator is an adiabatic reversible process The pressure and temperature before the regulator (gas bottle) are taken to be p0 and T0, respectively; the pressure and temperature after the regulator are p1 and T1, respectively Because the JouleThompson coefficient α h of CO2 is positive at room temperature as in Equation (1), larger injection pressure p1 results in larger temperature T1 ∂ T ⎞ T1 – T0 ≈ > αh = ⎛⎝ ⎠ ∂p p1 – p0 (1) Second, we assume that the gas injector can be simplified as a convergent nozzle For ideal gas, the critical 534 S QU, K DENG, L SHI and Y CUI values of nozzle velocity V , mass flow rate m· , momentum flux Momflux , and total momentum Momtotal are shown in equations (2)~(5) (2) V kRT1 cr cr cr = m· cr = Mom k ⎛ ⎞ p A -RT ⎝ k ⎠ +1 flux = m· V cr k+1 2(k – 1) p T ∝ (3) k - cr = -⎞ p kA⎛⎝ k ⎠ ∝p +1 k–1 (4) (5) ∆mV ∝ T1 where k is the adiabatic coefficient, R is the gas constant of ideal gas, A is the nozzle area, and ∆m is the total injection quantity Equations (2)~(5) indicate that critical velocity and total momentum are only related to injected gas temperature T , and momentum flux only relates to injected gas pressure p To simplify the analysis, we assume that equations (2)~ (5) are applicable to CO Under the second assumption, the larger the injection pressure, the larger the momentum flux Since larger injection pressure p results in larger temperature T , and larger T results in larger total momentum, then larger injection pressure also results in larger total momentum A jet with larger momentum flux and momentum provides stronger disturbance to the air-fuel mixture, resulting in a more homogeneous mixture; therefore, larger injection pressure leads to lower NO emission and smoke opacity Moreover, with the same CO injection quantity, the largest injection pressure will accelerate the injection process and shorten the injection duration, which has a similar effect as advancing injection timing This also contributes to emission reduction Figure 11 shows the cylinder pressure and the heat release rate at different injection pressures (1 MPa, MPa, MPa) All three cases have the same CO injection timing (−200 CA ATDC) As shown in the figure, increasing injection pressure delays SOC The trend is similar to that of advancing injection timing Mom total = cr 1 1 FUTURE WORK In this paper, diesel HCCI combustion was realized by injecting diesel during the negative valve overlap period The injected fuel utilized the heat of internal EGR and piston to vaporize, forming a homogeneous mixture before ignition By analyzing the effect of fuel injection timing, the authors proposed to use −365 CA ATDC as the fuel injection timing In the subsequent three sections, CO injection timing, quantity, and pressure were independently studied at a specific load condition (baseline case, IMEP = 4.7 bar, NO emission of 600 ppm) The results revealed that either advancing injection timing or increasing injection quantity can retard the start of combustion and reduce NO emission, while increasing injection pressure enhanced cylinder mixing and had an effect similar to advancing injection timing Since CO in-cylinder injection not only has the advantages of external EGR but also its own merits, such as extra disturbance to the air fuel mixture and quick response, it seems that it is a promising method to control HCCI combustion However, more work needs to be done to reach this conclusion Future work includes the following aspects: (1) a study of the effects of injection direction; (2) a study of the interactions between CO jet flow and incylinder air flow by CFD tools, and (3) expanding HCCI upper load limit o x x 2 CONCLUSION x 2 o (1) The choice of fuel injection timing during negative valve overlap needs to satisfy two criteria First, fuel injection timing should advance to maximize the utilization of heat of internal EGR Second, injection timing should not be premature to avoid early ignition and rough combustion (2) With the advance of CO injection timing, peak cylinder pressure, mean gas temperature, and pressure rise rate decrease; starts of LTR and HTR are delayed With CO injection pressure at MPa and cyclic injection quantity at 0.092 g, NO reaches its lowest level at CO injection timing of −240 CA ATDC, reduced by 91% compared to the baseline case (IMEP = 4.7 bar, NO emission of 600 ppm) (3) With the increase of CO injection quantity, peak mean gas temperature and peak pressure decrease and SOC is delayed With cyclic CO injection quantity of 0.14 g and injection pressure at MPa, SOC is delayed by CA, indicated thermal efficiency is improved, and NO is reduced by 97% compared to the baseline case (4) CO injection pressure determines the extent of disturbance on the cylinder mixture High injection pressure results in large momentum flux and total momentum, and hence, it is useful to increase mixture homogeneity, resulting in reduced NO and smoke emission 2 x o x 2 o x Figure 11 Effects of injection pressure on cylinder pressure and heat release rate x EFFECT OF DIRECT IN-CYLINDER CO2 INJECTION ON HCCI COMBUSTION ACKNOWLEDGEMENTS−The authors wish to acknowledge the financial support of National Key Fundamental R&D Programs (973 projects, 2007CB210003) and National Nature Science Foundation (Grant No 50406016) REFERENCES Choi, C Y and Foster, D E (1995) In cylinder augmented mixing through controlled gaseous jet injection SAE Paper No 952358 Hardy, W L and Reitz, R D (2006) A study of the effects of high EGR, high equivalence ration, and mixing time on emissions levels in a heavy-duty diesel engine for PCCI combustion SAE Paper No 2006-01-0026 Kamimoto, T., Osako, S and Matsuoka, S (1983) An air cell DI diesel engine and its soot emission characteristics SAE Paper No 831297 Kimura, S., Aoki, O., Kitahara, Y and Aiyoshizawa, E (2001) Ultra-clean combustion technology combining a low-temperature and premixed combustion concept for meeting future emission standards SAE Paper No 200101-0200 Kitamura, T., Ito, T., Senda, J and Fujimoto, H (2002) Mechanism of smokeless diesel combustion with oxygenated fuels based on the dependence of the equivalence ration and temperature on soot particle formation Int J Engine Research 3, Kurtz, E M and Foster, D E (1998) Exploring the limits of improving di diesel emissions by increasing in-cylinder mixing SAE Paper No 982677 535 Kurtz, E M., Mather, D K and Foster, D E (2000) Parameters that affect the impact of auxiliary gas injection in a DI diesel engine SAE Paper No 2000-01-0233 Nagano, S., Kawazoe, H and Ohsawa, K (1991) Reduction of soot emission by air-jet turbulence in a DI diesel engine SAE Paper No 912353 Okude, K., Mori, K., Shiino, S and Moriya, T (2004) Premixed compression ignition (PCI) combustion for simultaneous reduction on NOx and soot in diesel engine SAE Paper No 2004-01-1907 Onishi, S., Jo, S H., Shoda, K., Jo, P D and Kato, S (1979) Active thermo-atmospheric combustion (ATAC) - A new combustion process for internal combustion engines SAE Paper No 790501 Peng, Z., Zhao, H and Ladommatos, N (2003) Effects of air/fuel ratios and EGR rates on HCCI combustion of nheptane, a diesel type fuel SAE Paper No 2003-01-0747 Qu, S., Deng, K Y., Cui, Y and Shi, L (2008) Effects of carbon dioxide in-cylinder injection on premixed charge compression ignition combustion J Automobile Engineering, Proc IMechE, Part D, 222, 8, 1501−1511 Shi, L., Deng, K and Cui, Y (2005) Study of diesel-fueled HCCI combustion by in-cylinder early fuel injection and negative valve overlap J Automobile Engineering, Proc IMechE, Part D, 219(D10), 1193−1201 Shi, L., Deng, K and Cui, Y (2007) Combustion stability of diesel-fueled HCCI Int J Automotive Technology 8, 4, 395−402 Copyright © 2009 KSAE 1229−9138/2009/048−02 International Journal of Automotive Technology, Vol 10, No 5, pp 537−544 (2009) DOI 10.1007/s12239−009−0062−9 EFFECTS OF GASOLINE, DIESEL, LPG, AND LOW-CARBON FUELS AND VARIOUS CERTIFICATION MODES ON NANOPARTICLE EMISSION CHARACTERISTICS IN LIGHT-DUTY VEHICLES C L MYUNG , H LEE , K CHOI , Y J LEE and S PARK 1) 1) 1) 2) 1)* School of Mechanical Engineering, Korea University, Seoul 136-701, Korea Korea Institute of Energy Research, 102 Gageong-no, Yuseong-gu, Daejeon 305-343, Korea 1) 2) (Received November 2008; Revised 16 March 2009) ABSTRACT−This study was focused on experimental comparisons of the effects of various vehicle certification modes on particle emission characteristics of light-duty vehicles with gasoline, diesel, LPG, and low-carbon fuels such as bio-diesel, bioethanol, and compressed natural gas, respectively The particulate matter from various fueled vehicles was analyzed with the golden particle measurement system recommended by the particle measurement programme, which consists of CVS, a particle number counter, and particle number diluters To verify particle number and size distribution characteristics, various vehicle emission certification modes such as NEDC, FTP-75, and HWFET were compared to evaluate particle formation with both CPC and DMS500 The formation of particles was highly dependent on vehicle speed and load conditions for each mode In particular, the particle numbers of conventional fuels and low-carbon fuels sharply increased during cold start, fast transient acceleration, and high-load operation phases of the vehicle emission tests A diesel vehicle fitted with a particulate filter showed substantial reduction of particulate matter with a number concentration equivalent to gasoline and LPG fuel Moreover, bio-fuels and natural gas have the potential to reduce the particulate emissions with the help of clean combustion and low-carbon fuel quality compared to non-DPF diesel-fueled vehicles KEY WORDS : Particulate matter, Nanoparticles, Diesel particulate filter, Differential mobility spectrometer, Condensation particle counter, Low-carbon fuels INTRODUCTION start phase and at high-speed operating conditions; thus, the particle formation mechanism of spark ignition engines have been investigated (Choi et al., 2006; Kayes and Hochgreb, 1999; Ristovski et al., 2000) PM has been emphasized as a toxic air contaminant (TAC) by the California air resources board (CARB) Moreover, the developed countries have been focusing on the effects of a variety of airborne particulates on health risks Current legislative exhaust emissions standards restrict particle emission in terms of the total mass discharged per kilometer traveled Regulations based on total mass are an effective way to control large particles; however, fine particles contribute little to the total mass of particulate matter emissions (Andersson et al., 2001 and 2004) In this context, the international particle measurement programme (PMP) has been developing a new particle size measurement technique to complement or replace massbased PM measurement procedures Final inter-laboratory correlation exercise (ILCE) results on particle number for LDV showed that particle number concentrations emitted from non-diesel particulate filter (DPF) diesel-fueled vehicles (E+13 particles/km) were much higher than from multipoint injection (MPI) gasoline engines (E+11 particles/km) and slightly higher than from gasoline direct injection engines Diesel-powered engines have advantages of increased engine power output, fuel economy, and higher durability than spark ignition engines In addition, they can reduce emissions such as hydrocarbons and carbon monoxide Diesel engines are widely used in heavy-duty trucks, buses, engine generators, etc., as they have fewer penalties in performance and emissions In spite of the many advantages, the emissions of smoke and particulate matter (PM) from heavy-duty engines are a big drawback and are thus the focus of many environmental researchers From the viewpoint of health, PM emitted from diesel engines causes adverse health effects, and recent studies have announced that PM in the atmosphere is an important factor in mortality and morbidity (Dockery et al., 1993; Giechaskiel et al., 2007; Hagena et al., 2006; Ostro, 1984; Pope et al., 1992; Takeda et al., 1995; Vaaraslahti et al., 2005) In addition to diesel particles, conventional gasoline and low-carbon fuels such as liquefied petroleum gas (LPG), compressed natural gas (CNG), and various bio-fuels emit a considerable amount of nanoparticles during the cold *Corresponding author e-mail: spark@korea.ac.kr 537 538 C L MYUNG et al (E+12 particles/km), but particle numbers with DPF fitted vehicles showed results equivalent to gasoline engines (Andersson , 2007; Lee , 2008; Roberto , 2007) In diesel vehicle emissions, PM consists of tiny solid particles and liquid droplets ranging from a few nanometers to around one micrometer in diameter (below 1,000 nm) PM size distributions are generally classified as trimodal The three modes are the nucleation mode, accumulation mode, and coarse mode The nucleation mode is typically composed of nanoparticles in the 5~50 nm diameter range This mode consists of volatile organic and sulfur compounds formed during the exhaust dilution and cooling process The accumulation mode ranges from 50 to 1,000 nm and usually consists of particles that have been deposited on cylinder walls and exhaust system surfaces Finally, the coarse mode, rarely emitted in internal combustion engines, is composed of particles with diameters greater than 1,000 nm (Kittelson, 1998) The goal of this research is to verify particle number and size distribution characteristics under various vehicle certification modes such as the new European driving cycle (NEDC), federal test procedure (FTP)-75, and the highway fuel economy test (HWFET) modes using conventional fuels, including low-carbon fuels that are widely used in Korea automotive markets et al et al et al EXPERIMENTAL APPARATUS AND METHOD 2.1 Test Fuels and Vehicle Descriptions Figure shows the schematic diagram of the vehicle experimental apparatus used to analyze the particle number concentration and particle mass under the NEDC, FTP-75, and HWFET modes To minimize fuel variation during test periods, gasoline and diesel fuels were supplied from the same filling station with one-batch preparation Summer LPG and CNG for urban buses were used for gas-fueled vehicles In the case of the 2.0 liter diesel engine, advanced DPF meeting the EURO emission regulation was equipped In addition, a 2.5 liter diesel engine run on 50% of biodiesel fuel was tested Additionally, a retrofitted 2.4 liter bifueled CNG vehicle that can automatically switch between gasoline and natural gas fuel was also tested The test procedure for the bi-fueled CNG vehicle was as follows To assess each fuel effect on particle formation in the CNG vehicle, the gasoline fuel mode was tested first Then, the natural gas mode was selected using the fuel selection switch In this condition, natural gas was automatically changed from gasoline during the NEDC mode when the engine coolant temperature reached a target value In the case of the ethanol flexible fuel vehicle (FFV), the ethanol content was varied from gasoline to E85 (85% ethanol + 15% gasoline) To save time, only the NEDC test mode was used for low-carbon fuels (FFV, bio-diesel) Test fuel properties and vehicle specifications are summarized in Figure Schematic diagram of vehicle experimental system Table Properties of ethanol blended gasoline fuel Property items Gasoline E85 RON 93.2 > 100 Distillation temperature (ºC) 10 vol% 51.9 73.9 50 vol% 80.0 78.0 90 vol% 153.9 78.7 Sulfur content (mg/kg) 18.00 0.90 Oxygen (weight%) 1.75 30.00 Table Properties of bio-diesel fuel Property items ULSD Cetane number 55.9 826.9 Density@15ºC (kg/m ) 2.8 Viscosity (mm2/s) Sulfur content (weight %) 0.022 Lubrication@60ºC (µm) 336 BD50 55 850 3.2 0.014 164 Table 1, Table 2, and Table 2.2 Particle Analyzer and Sampling System The flow rate of the diluted exhaust gas through the CVS tunnel was 20 m3/min at standard reference conditions (i.e., 20ºC and bar) The primary dilution air was passed through a high-efficiency particulate air (HEPA) filter to minimize the particle effect of the background level of an emission facility A sample probe for particles was fitted near the center line in the dilution tunnel, and a cyclone was used as a preclassifier to remove the particles with diameters greater than 2.5 µm in the CVS tunnel The number of particles emitted from the test vehicle was counted using the golden particle measurement system (GPMS) which is recommended by PMP Figure represents the GPMS and particle mass system 630 D.-I LEE and H.-C LIM until the vehicle stops Second, in order to see the effects of corrosion and/or erosion-corrosion on the impeller, we created a dynamic setup by building a water-coolant chamber that submerges the impeller and creates similar loading conditions as would be found in normal vehicle operation EXPERIMENTAL APPARATUS & METHOD Figure Impeller blades damaged due to corrosive erosion ternal systems, such as steam boiler pumps, the propellers of water vessels, and the blades of a wind turbine (Lee, 2008) Figure shows an example of the typical damage to an impeller observed in the water pump of an automotive vehicle This damage is known to be usually caused by both corrosion and erosion-corrosion effects over time Because the working fluid in the engine basically includes a commercial coolant, one might presuppose that the corrosion effect would be negligible and the erosion-corrosion effect ( , cavitation) the dominant parameter As far as we are concerned, however, whether either of them is dominant or similar in magnitude is still questionable One recent paper deals with the corrosion-erosion effect (Ariely and Khentov, 2006) in the impeller inside a water pump of a power plant, where they observed pits and a dendrite pattern on the surface, but the study was narrow in scope and limited to specific materials Therefore, we aimed to conduct an experimental study on the corrosion and/or erosion-corrosion effect of an impeller in the water pump of an automotive engine The results will have broader applications, such as for mid- to large-sized impellers in various industries In order to achieve these objectives, various working conditions of a real water pump mounted on a vehicle were modeled into the experimental condition First, in order to observe the corrosive effect under static conditions, commercial coolants containing mainly ethylene glycol were used, with four different ratios of mixture (water and coolant) so that we would know how far the corrosion on the impeller surface had gone through However, the real impeller in a water pump is not static, but rotating, providing coolant e.g Figure (a) Specimen sample; (b) 100% tap water and mixture of 1:1 antifreeze solution and tap water 2.1 Specimen Analysis The experiments were carried out under various conditions that included different water-coolant mixtures under static conditions and with dynamic rotation The specimens used in the static observations were the main constitutive materials from the impeller of a water pump from a real automotive vehicle In order to make our methods valid, most of the materials were cut to pieces of the same size and thickness −2.0×2.0 mm2 (width×height) and around mm thickness (Figure 2) Characterization of the specimens was very important to start the current study and analyze the main constituents of the specimen Therefore, more accurate inspection allowed for a better prediction of the corrosion effect The impeller specimen was examined by a Hitachi Scanning Electron Microscope (SEM) S-2400, located at the Pukyong Nat'l University of South Korea Basically, a SEM is a method for imaging the surface in detail A beam of electrons generated in a vacuum chamber are focused to a very fine point on the sample by the objective lens When it hits the sample, secondary and backscattered electrons are produced, collected and converted into a voltage and/or an image The rated voltage and the beam current applied for analyzing the specimens were 20 kV and 150 picoamps, respectively The analysis results for the impeller material are shown in Table The impeller was made of around 91% cast iron (or usually Fe-1.7 wt.% C alloy) by weight, 8.8% carbon by weight, and other substances The basis of the quantitative microanalysis is the ratio of the X-ray intensity from an element in a sample of unknown composition to that of a standard The K-ratio in Table 1, defined as specimen / standard , is the ratio of characteristic intensities measured on the specimen and standard There exist “matrix effects” arising from the nature of the interactions of the electrons and X-rays with matter that modify the measured intensities and depend on the unknown composition of the specimen A variety of approaches have been used to calculate the correction factors for these matrix effects In spite of the importance of the K-ratio, as well as the atomic molar ratio, however, main attentions were not seriously made in these parameters I I Table SEM/EDX material analysis report of specimen Element Weight [%] K-ratio Atomic [%] C 8.76 0.0182 30.1 Fe 91.24 0.8905 69.1 EROSION-CORROSION DAMAGES OF WATER-PUMP IMPELLER 2.2 Surface Observation There are several methods with which to evaluate the surface corrosion phenomena of a material (Fontana, 1978) One method applied in this study was measurement by high resolution microscopy The other, a weight measurement, which will be explained in the next chapter The microscopy (KSM-BA3) applied to the diagnosis of the corrosive surface yields effective observing magnifications of 40× to 900× When scanning a corrosive surface, the primary concern with high resolution microscopy was that the visibility gradually decreased as the corrosion on the surface progressed This was certainly due to the coolant containing an anticorrosive Therefore, in order to get clearer images, low resolution images were mainly relied upon for the results 2.3 Weight Measurement The second method applied to verify the corrosion effect is weight measurement Generally, the specimen does lose weight as corrosion progresses Weight measurements were made with a high precision scale with an error around 1/ 10,000 kg Even though weight measurement is already a well-known experimental method, corrosive action has little short-term effect, taking at least months and sometimes over a year for a given specimen Therefore, when a serious damage does happen it is too late to evaluate the corrosion The coefficient of weight loss is defined as the ratio of the pure weight loss to the exposed surface area, as follows: Rw = - Original weight – Elapsed weight Exposed area (1) We originally normalized equation (1) by weight, but once it was realized that corrosion usually occurs on the surface, the surface area was used as the denominator 2.4 Water-coolant Mixture To look into the corrosive effects of the coolant, the working fluids used in this study were mixtures of coolant and water For a typical vehicle, an optimal coolant combination is a 1:1 mixture of anticorrosive (and/or antifreeze) and water Many experts claim that any greater or lower concentration does not significantly change the efficiency of the coolant, so here we not consider the cooling effect in the case of pure coolant The working fluid for the coolant used in this study consisted of 92% ethylene glycol (ethane-1,2-diol or HOCH2CH2OH), around 3.5% water (H2O), and several different additives, such as 2% carboxylic acid salt (CnHm-COONa), 1% sodium phosphate (Na3PO4) and a very small amount of potassium hydroxide (KOH), benzo-triazol (C6H5N3), sodium molybdate (Na2MoO42H2), and sodium nitrate (Na-NO3) Liquid coolant usually contains sodium nitrate and TEA (triethanolamine) for anticorrosive purposes and some alkaline compounds to prevent the fluid from acidifying The dilutant solution for the coolant was regular tap water For objective validity, the 631 Figure Photograph and schematic diagram of corrosion test apparatus experiments were carried out under different conditions of water and mixtures of water and coolants, based on water contents of 25%, 50%, 75%, and 100% water 2.5 Erosion-corrosion Observation Erosion-corrosion occurs when a metal is not only attacked by the relative motion between a working fluid ( , coolant) and the metal surface, but also processed by chemical reactions (such as corrosion) Although chemical processes occur and can be critically important, many examples of erosion can be attributed to mechanical effects, such as wear, abrasion, and scouring In real automotive vehicles, the impeller is usually placed in water or watercoolant mixtures inside a water pump in the engine, meaning that it is under dynamic conditions as well as a static corrosive environment Predicting erosion-corrosion phenomena precisely is therefore not easy In order to sort out this problem from an engineering point of view, an appropriate approach could be the observation of the erosioncorrosion traits of the rotating impeller In addition, to represent the real working environment in a vehicle, the experiments were perofrmed for 3~4 hours per day while changing the rotation speeds of the impeller Figure shows the schematic diagram and a photograph of the working chamber including the combination of a motor and an impeller The tests were controlled at a range of room temperatures around 25±3oC Most of the test materials were obtained from a real vehicle, including the motor The impeller was attached to a long steel support and the running speeds were changed among four settings between around 2500 to 3770 rpm (Table 2) i.e RESULTS AND DISCUSSION 3.1 Corrosion Observation Table RPM speeds of impeller measured by a tachometer 1st 2nd 3rd 4th Average Level 2488 2482 2472 2470 2478 rpm (41 Hz) Level 2759 2780 2750 2750 2759 rpm (46 Hz) Level 3085 3071 3082 3068 3068 rpm (51 Hz) Level 3770 3773 3771 3776 3772 rpm (63 Hz) 632 D.-I LEE and H.-C LIM Figure Temporal variation of the corrosive surface (100× magnified) of specimen in tap water (left figure) and in 50~50 coolant-tap water mixtures after: (a) days (top); (b) 10 days (middle); (c) 25 days (bottom) Figure shows 100× magnified photographs indicating temporal surface variation after about one month For the sake of convenience, the photographs presented in the paper only compare the cases of the 1:1 water-coolant mixtures and 100% water The 1:1 mixture is meant to approximate the ratio found in a typical vehicle, whereas 100% water corresponds to an extreme condition so that we can visually compare the differences on the surface With pure tap water (Figure on the left-hand-side), the corrosive rate is expected to be greater than for the watercoolant mixture In fact, as is visible in the figure, the surface color itself gets brighter, minute crystals formed in places, and the surface appears to be on the verge of rapidly corroding On the other hand, for the 1:1 water-coolant mixture (Figure 4, right), minute surface granularity appeared, but the variation, compared to pure tap water, was considerably reduced These phenomena on the impeller surface appear to be typical (Trethewey and Chamberlain, 1988) In other words, the corrosion rates depend on the type of alloy used ( , Fe and C in this study), a phenomenon often referred to as selective attack It is therefore possible to include certain forms of corrosion on the surface such as pitting corrosion and/or minute granularity i.e 3.2 Weight Loss Measurement As mentioned above, a strong focus should be given to the crucial role of chemical reactions on corrosion in the different mixtures Therefore, variations in weight loss, Figure Variation of weight reduction per unit area: (a) 25:75%; (b) 1:1%; (c) 75:25% coolant-water mixture which is a standard measure of corrosion, were observed Figures and show the variation in weight loss per unit area ( [kg/m2]) under different water:coolant ratios: 1:3, 1:1, 3:1 and pure water As can be seen in Figure 5, the common trend of corrosion in the mixtures of water and coolant was generally constant during the test Note that the specimens initially gained weight due to the initial formation of rust, but they approached a constant weight over time The solid line used in Figure indicates the linear regression fit-line and is described in the following linear equation, Rw c × days β (2) where and β are constants dependent on the corrosive material and the experimental conditions Two sets of specimens (specimens A and B) in each condition were used in order to reduce the statistical errors Note here that the weight reduction of the specimen depended on many parameters, such as the exposed area of the specimen, the coolant constituents, and the ambient/coolant temperature Therefore, the value of the non-zero intercept along the axis (α on the x-axis and β on the y-axis) cannot be the Rw = + c EROSION-CORROSION DAMAGES OF WATER-PUMP IMPELLER Figure Variation of weight reduction per unit area in 100% water 633 Figure Variation of weight reduction of a rotating impeller in the 100% tap-water Interestingly, after around 2~3 months, the rate of weight loss rapidly increased The observations were again fitted with a linear curve using following equation: (3) Rw = d × ( days – α) where and α are constants dependent on the erosioncorrosion characteristics of the impeller The constant in the study was 0.2 The immediate implication of these results is that with a moving body under dynamic conditions, the figure would yield a linear slope Interestingly, there is a sudden change of slope after a period of time (around 70 days) Accompanying this phenomenon, there was a gradual change in the coolant color during the experiment, an effect which could originate from an ingredient of the coolant and could make the erosion-corrosion damage higher The original color of the coolant was usually green, but the color finally turned gray In addition, the pH was measured at the beginning and near the end of the experiment Interestingly, at the beginning, the pH was about 10.0 ( , alkaline) and it gradually decreased to 7.2 by the end of the experiment ( , the pH of tap water is close to 7.0) It can be conjectured that this condition could induce erosion-corrosion damage on the surface of the impeller However, a conclusion regarding the erosioncorrosion mechanism from this result is premature For a more in-depth study, observations of weight loss under only pure water were also carried out, as shown in Figure A linear, rapid weight loss ( , ~ 0.7) was observed at the beginning of the experiment, a rate about 2~3 times higher than for the impeller in water under both static and dynamic conditions As a reference for the erosion-corrosion effect, Figure shows the temporal surface view of the impeller tested in 100% pure water after 100 days As can be seen in the figure, most of the steel surface is pitted and clearly rusty, so that the erosion-corrosion phenomena are noticeable d d Figure Variation of weight reduction of a rotating impeller in the 1:1% mixture of coolant and water exact determinant or may not be useful so that the length of the elapsed time can be extended or shrunken Interestingly, the temporal variation under 100% water (Figure 6) showed a rapid increase in weight loss that was precisely fitted with a linear regression (one is 0.38 and the other 0.25) There was a distinct change in the slope at around day 70, as well as smaller variations of around 0.2~0.4 depending on the ambient conditions However, in the other mixtures, the weight loss at around two months (four months for 1:1 mixtures) was negligible 3.3 Erosion-corrosion on the Rotating Impeller Figure shows the temporal weight loss variations for the rotating impeller, with the purpose of demonstrating the dynamic effects of the impeller on the erosion-corrosion The experiment was run 3~4 hours per day while changing the rotation speed of the impeller The working fluid in the figure was a water-coolant mixture of 1:1 Kim (2007) reported that at the beginning of their experiment, the weight loss significantly increased parabolically ( w = × n ) In the current experiment such an increase was not observed, but for around months of time there was no change in the weight loss rate, an effect which seems to be due to the corrosion-proof ingredients of the coolant et al R d i.e e.g i.e d days CONCLUSION This experiment aimed to study the corrosion and erosion- 634 D.-I LEE and H.-C LIM Figure Temporal surface view of impeller tested at the 100% tap-water: (a) After around 100 days; (b) Snapshot of the tip of a impeller showing the erosion-corrosion effect corrosion effects on an impeller in the water pump of an automotive engine To achieve this objective, the working conditions of an actual vehicular water pump were artificially simulated in the experimental conditions To observe the corrosion effects under static conditions, observations were made of the surface were made using high-resolution microscopy for different ratios of water and coolant From the microscopy results, most of the steel surface submerged in 100% water had minute granularity, such that the corrosion phenomena were noticeable as time passed In addition, the total weight loss of the specimen in the water increased linearly over time, whereas the samples in mixtures of water initially gained weight and then approached a constant weight Temporal weight loss variations on the rotating impeller also showed a linear trend for the erosioncorrosion effect The weight loss of a specimen in water and/or watercoolant mixtures is evident at a laboratory scale and may continue over time or saturate to a certain value after up to several years, although this latter statement cannot be proven from the present data Consequently, the choice of impeller and water-coolant mixture should be undertaken with caution in the automotive industry ACKNOWLEDGEMENT−The authors wish to thank the help of Mr Jae-Wook KIM (PuKyong Nat’l Univ.) and Ms Su-Jin LEE (Pusan Nat’l Univ.) This work was supported by the Korea Research Foundation (KRF) grant funded by the Korea government (MEST) (No 2009-0076096) This work was also supported by the National Research Foundation of Korea (NRF) through a grant provided by the Korean Ministry of Education, Science & Technology (MEST) in 2009 (No K20607010000) REFERENCES Anagnostopoulos, J S (2008) A fast numerical method for flow analysis and blade design in centrifugal pump impellers Computers & Fluids, Article in Press Ariely, S and Khentov, A (2006) Erosion corrosion of pump impeller of cyclic cooling water system Engineering Failure Analysis, 13, 925−932 Fontana, M G (1978) Corrosion Engineering McGrawHill New York Karassik, I J., Messina, J P., Cooper, P and Heald, C C (2007) Pump Handbook McGraw-Hill New York Kim, J W., Lim, H C and Lim, U J (2007) Study on the erosion-corrosion damages of pump impeller KSME Spring Conf 102−107 Lee, C K (2008) Corrosion and wear-corrosion resistance properties of electroless ni.p coatings on GFRP composite in wind turbine blades Surface & Coatings Technology, 202, 4868−4874 Li, P., Cai, Q and Wei, B (2006) Failure analysis of the impeller of slurry pump used in zinc hydrometallurgy process Engineering Failure Analysis, 13, 876−885 Shalaby, H M (2008) Failure of hastelloy c-276 pump impeller in hydrochloric acid Engineering Failure Analysis, 15, 543−546 Torregrosa, A J., Broatch, A., Olmeda, P and Romero, C (2008) Assessment of the influence of different cooling system configurations on engine warm-up, emissions and fuel consumption Int J Automotive Technology 9, 4, 447−458 Trethewey, K R and Chamberlain, J (1988) Corrosion: For Students of Science and Engineering Longman Scientific & Technical, UK Copyright © 2009 KSAE 1229−9138/2009/048−15 International Journal of Automotive Technology, Vol 10, No 5, pp 635−644 (2009) DOI 10.1007/s12239−009−0075−4 STUDY OF RCM-BASED MAINTENANCE PLANNING FOR COMPLEX STRUCTURES USING SOFT COMPUTING TECHNIQUE Y T SON , B Y KIM , K J PARK , H Y LEE , H J KIM and M W SUH 1) 1) 2) 3) 1) 4)* Graduate School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi 440-746, Korea Urban Transit Standardization Research Team, Korea Railroad Research Institute, 360-1 Woram-dong, Uiwang-si, Gyeonggi 437-757, Korea LRT System Research Team, Korea Railroad Research Institute, 360-1 Woram-dong, Uiwang-si, Gyeonggi 437-757, Korea School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi 440-746, Korea 1) 2) 3) 4) (Received 20 July 2007; Revised 24 March 2009) ABSTRACT−To guarantee the efficiency of maintenance strategies for a complex structure, safety and cost limitations must be considered This research introduces RCM-based (Reliability Centered Maintenance) life cycle optimization for reasonable maintenance The design variable is the reliability of each part, which consists of a complex structure, while the objective is to minimize the total cost function in order to maintain the system within the desired system reliability This research constructs the cost function that can reflect the current operating condition and maintenance characteristics of individual parts by generating essential cost factors To identify the optimal reliability of each component in a system, this paper uses a NeuroEvolutionary technique Additionally, this research analyzes the reliability growth of a system by using the AMSAA (Army Material Systems Analysis Activity) model to estimate the failure rate of each part The MTBF (Mean Time Between Failure) and the failure rate of the whole system, which is responding to the individual parts, are estimated based on the history data by using neural networks Finally, this paper presents the optimal life cycle of a complex structure by applying the optimal reliability and the estimated MTBF to the RAMS (Reliability, Availability, Maintainability, and Safety) algorithm KEY WORDS : RCM (Reliability Centered Maintenance), Complex Structure, RGA (Reliability Growth Analysis), Cost Function, Neuro-Evolutionary Technique, Maintenance Plan, Life Cycle, RAMS (Reliability, Availability, Maintainability, and Safety) INTRODUCTION (Garcia Marquez , 2003) By applying this concept to complex structures, it can be altered to establish the life cycle for supporting each sub-structure This strategy uses the reliability concept that is called RCM (Reliability Centered Maintenance) (Smith, 1993) This research presents RCM-based life cycle optimization for reasonable maintenance of complex structures This research applies soft computing techniques, namely the Neural Network (NN) and the Evolutionary Algorithm (EA), in order to find the reliability allocation to enable a search for a flexible optimum in the nonlinear domain The technique is called the Neuro-Evolutionary Algorithm In the optimization, the first step is to analyze the historical failure data of the system The analysis is helpful for calculating several reliability indices of individual parts in a complex structure: failure rate, MTBF (Mean Time between Failures), and MTTR (Mean Time to Repair) The indices, such as the failure rate, MTBF, and MTTR, of the individual parts can be obtained with RGA (Reliability Growth Analysis) This research estimates the reliability and maintainability from the indices with the RAMS algorithm The MTBF, failure rate, and reliability of the whole system that responds to the individual parts are estimated by using et al Structures such as aircraft, automobiles, vessels, and railroads are complex structures that consist of electrics and machinery Accidents due to these defects cause serious social problems Therefore, it is essential to ensure that there is security for complex structures For such assurance, preventive maintenance of the structures is of utmost importance However, maintenance costs generally take about 60% of the total operational cost because of the weak basis for repetitious failure and maintenance cycles (Lee , 2003) Society requires a reasonable maintenance classification that can maintain a level of functionality without critical failure and that can reduce the maintenance and supportive costs of the complex structure To consider both safety and cost limitations, this research introduces the concept of reliability, which can be used to set up reasonable maintenance strategies In general, reliability is defined as the ‘probability that an item will perform a required function without failure under the stated conditions for a stated period of time’ et al * Corresponding author e-mail: suhmw@skku.edu 635 636 Y T SON et al the neural network The next step is to establish a problem for optimal reliability allocation (Adamantios, 2000) The cost function, which is the objective, is defined with respect to the reliability and maintainability of individual parts and the whole system The system reliability, which is calculated by the reliability relationship of the individual parts, must satisfy the desired value throughout the optimization process Finally, this research presents a method to establish the proper maintenance plan by using the data of the life cycles of individual parts, which are derived from the use of optimal reliability and reliability indices in the inverse analysis of the fundamental reliability function LIFE CYCLE EVALUATION 2.1 Reliability Growth Analysis In general, a new device contains some deficiency factors Because of these deficiencies, the initial reliability may be below the desired reliability of the system In order to identify these deficiencies and to predict the system’s reliability in the future, reliability growth analysis (RGA) has been widely used (Musa, 1975) This research introduces the AMSAA (Army Material Systems Analysis Activity) model (Reliasoft, 2005), which has an underlying Weibull distribution, because it can estimate the distribution parameters for analyzing reliability growth under any condition for both completed and censored failure data With this result, we can monitor the reliability indices, such as failure rate and MTBF, versus the time domain in the real number area The distribution parameters, which consist of λ and β , can be calculated with Equation (1) that is derived from the maximum likelihood estimators In Equation (1), λ is a scale parameter, which determines the mathematical scale of the distribution, while β is a shape parameter, which affects the shape of the distribution If the shape parameter, β , of any part is greater than one, the curve of the failure rate versus the time domain would show a decreasing trend The curve of the MTBF illustrates an increasing-trend n n λ = -β, β = -T n ln T – T ∑ (1) n i i =1 where λ is a scale parameter, β is a shape parameter, T is the elapsed operation time, and n is the accumulative failure number Failure rate and MTBF are determined by using Equation (2) and both of the distribution parameters λ m = - T c 1–β (2) , λ =λT β – c where m is the cumulative MTBF and λ is the cumulative failure rate c Figure Bath tub curve the main consideration in system maintenance Figure shows a complete failure-rate curve that contains the useful life period to which Equation (3) can be applied The characteristic failure curve of the individual parts that are all placed in service at the same time includes a break-in period, a chance failure region, and a wear-out zone, as shown in Figure After an initially high failure rate, the breakdown rate becomes constant The failures that occur in this period are random and follow statistical chance laws Random breakdowns will produce a constant failure rate The constant failure rate period represents the useful working life of the part; an expression for this part of the failure rate curve should be developed The rate eventually begins to rise again because individual parts start to wear out The AMSAA model can describe the phases from early failures to constant failures In the random breakdown period, the reliability of individual parts can be written as: (3) R = e –λ Equation (3) is the basic expression for the reliability of an individual part during its useful life A term that is often used in reliability studies is MTBF Equation (3) can be written as – / (4) R=e c T m c 2.3 System Reliability Analysis To analyze the reliability of a complex structure, reliability indices of the system must first be calculated from the failure rate or the MTBF of the members In a series system that is composed of n parts, the reliability indices of the system can be expressed with Equations (5) and (6) λ ( t ) = = m (t) s s ∑λ t n i i (5) ( ) =1 where λ (t) is the system’s failure rate, m (t ) is the system’s MTBF, and λ ( t) is the failure rate of the i-th part s s i c 2.2 Reliability Analysis for Individual Parts The reliability of individual parts in complex structures is T R (t)= s ∏ R t = ∏ exp –λ n n i i =1 ( ) ( i =1 s ⎛ ( t ) × t ) =exp ⎜ – ⎝ ∑λ t n i i =1 ⎞ ( ) × t⎟ ⎠ (6) where R (t) is the system’s reliability and R (t) is the s i STUDY OF RCM-BASED MAINTENANCE PLANNING FOR COMPLEX STRUCTURES reliability of the -th part In a parallel system that is composed of reliability indices are described as i ⎛ ∞ n parts, the ⎞ n (7) λ ( t) =1/ ⎜ ∫0 – ∏ ( – exp(λ ( t ) × t) )⎟ s i ⎝ Rs ( t ) ⎠ i=1 ⎛ ⎛ ∞ ⎛ n ⎞⎞ ⎞ ⎠⎠ ⎠ =exp⎜ 1/⎜ ∫0 ⎜ – ∏ ( – exp( λ ( t) × t ) )⎟ ⎟ × t⎟ ⎝ i ⎝ ⎝ i=1 (8) The failure rate, MTBF, and reliability of a system that consists of a mixture of series and parallel structures can be obtained by the combination of Equations (3)~(8), according to the work flow of the system Because the failure rate, MTBF, and reliability are in the time domain, it is possible to analyze the reliability growth due to the operation time of the complex structure =exp( –λ ( t) × t ) s RELIABILITY ALLOCATION n R i 3.2 Cost Function This research presents the total operational cost function of a system with many members First, we suppose that the total cost is the sum of the operational costs of the parts, and then define the system’s operational cost as the sum of the initial cost, repair cost, and maintenance cost The total cost function can be expressed as Equation (10), and the function has the weight factors to reflect the characteristics of the individual parts, respectively n : = ∑ C (R ) Minimize C maximum reliability is the same as 0.999, and the minimum is individually determined by the characteristic factors These factors are estimated by criticality, which means the degree to which the system’s function is affected when a part fails and the functional/structural importance of the part in the system Thus, as the importance and criticality of a part increases, the minimum of its reliability is required to be higher C 3.1 Definition of Optimization Problem This research allocates proper maintenance reliability of individual parts by using an optimization technique The optimization is to minimize the operation cost by meeting the desired reliability of a system Therefore, a layout of the optimization problem can be expressed with Equation (9): i = ∑ ( w1 × C i : Rs ≥ Rg (9) R i ,min ≤ R i ≤ R i ,max =1,2, , where is the total system operational cost, is the number of parts, i is the operational cost of the -th part, i is the reliability of the -th part, s is the system’s reliability, g is the desired system reliability, R ,max is the maximum reliability of the -th part, and R ,min is the minimum reliability of the -th part The inequality constraint is the desired system reliability This is the probability that a system can maintain a function without failure during a desired period of time The system reliability has been calculated by using Equations (5)~(8) that are based on the reliability relationship between a system and its members However, this conventional method can be difficult to apply when calculating the system’s reliability because it is almost impossible to define the reliability relationship between a system and its parts in the actual complex structures This is why we propose an approximation method to calculate the system’s reliability Therefore, this research constructs the relationship through an artificial technique, namely, the neural network out of the approximation methods The reliability of each part acts as a design variable, and its scope is limited by the side constraint condition The n C n C i i R R i i + w2 × C i repair + w3 × C R i mainenance (10) ) where initial is the initial cost function, repair is the repair cost function, maintenance is the maintenance cost function, 1i is the initial cost weight factor of the i-th part, 2i is the repair cost weight factor of the i-th part, and 3i is the maintenance cost weight factor of the i-th part Each cost function is defined as follows First, the initial cost means the total value from purchasing to installation It can be written as Equation (11) C C C w w w n , i initial i=1 i=1 s.t 637 C initial = ∑ w1 (11) i i=1 Second, the repair cost is a probabilistic cost about failure repair of only the i-th part (Ronald, 1997) It does not include failure that is not caused by the i-th part This means that the probabilistic intersection of the system’s reliability and unreliability of a part The total repair cost is the sum of the cost of each part, see Equation (12) i i Figure Effect of mˆ and function i Ri on the maintenance cost 638 C repair Y T SON et al = ∑w n ( 2i × i=1 R × (1 – R )) s (12) i Third, the maintenance cost is the total cost of maintaining or improving the reliability, as illustrated in Equation (13) (Adamantios, 2000) C ma int enance = ∑ n ⎛ ⎝ i=1 R – R ,min-⎞⎞ w3 × exp⎛⎝(1 – mˆ ) R – R ⎠⎠ i i i i ,max i i (13) where mˆ is the maintainability of the i-th part The maintainability of a part is the ease with which a part can be modified in order to correct faults or to improve performance The maintainability, mˆ , is expressed by Equation (14) In Equation (14), the MTTR (Mean Time To Repair) is defined as the average time that a part will take to recover from a non-terminal failure and can be obtained by analyzing the historical failure data i i mˆ × T⎞ - than 0.95 shows an exponential growth of the maintenance cost value Figure also shows the effect of mˆ on the maintenance cost function mˆ has a value that ranges from zero to one, and when mˆ equals one, the maintainability of the -th part is 100% Although some parts have the same reliability, a part that has a high mˆ requires more expensive maintenance costs than a part that has a low mˆ Finally, this research proposes that the total cost is a function of the sum of the initial, repair, and maintenance costs, like Equation (15) i i i i i i C = ∑ w1 + ∑ ( w2 × R × ( – R ) ) n n i i=1 + i s i i=1 ∑ n i=1 ⎛ ⎝ R – R ,min-⎞⎞ w3 × exp⎛⎝(1 – mˆ ) R – R ⎠⎠ i i i i ,max i i (15) MAINTENANCE CYCLE ESTIMATION (14) MTTR ⎠ where is the elapsed operational time The maintenance cost function complies with the rules as follows First, the maintenance cost to maintain the high reliability of a part is very high Second, the maintenance cost to maintain the low reliability of a part is very low Third, the slope of the maintenance cost function linearly increases if the reliability of a part increases Figure shows an example of the maintenance cost function In the area where the reliability is low, the maintenance cost has a small value and its slope seems to be nearly uniform On the contrary, in the area where the reliability is high, the maintenance cost increases with the increment of reliability In particular, the area where the reliability is greater Figure shows the optimization flow for estimating the proper maintenance cycle The optimal reliability is allocated to individual parts by using the soft computing technique, namely the Neuro-Evolutionary technique, within the range that satisfies the desired system reliability The technique can search a flexible optimum in the nonlinear domain The neural network can be trained to find the solution of a nonlinear problem, and the evolutionary algorithm can find the global minimum of a complicated optimization problem Finally, this research presents a method for establishing the proper maintenance plan that uses the life cycles of individual parts that are derived from the optimal reliability and the reliability indices by an inverse analysis of the fundamental reliability function Figure Flowchart of proposed RCM method 4.1 Neural Network Neural Networks (NN) are motivated to imitate the operation of the brain A network is composed of the individual neurons, the network connectivity, the weights that are associated with various interconnections between neurons, and the activation function for each neuron The network maps an input vector from one space to another The mapping is not specified but is learned The network is presented with a given set of inputs and their associated outputs The learning process is used to determine the proper interconnection weights while the network is trained to make proper associations between the inputs and their corresponding outputs Once trained, the network provides rapid mapping of a given input onto the desired output quantities This process, in turn, can be used to enhance the efficiency of the design process Consider a single neuron This neuron receives a set of inputs, , (where =1,2, , ) from its neighboring neurons and a bias value that is equal to one Each of the inputs has a weight (gain), , that connects the i-th and j-th units The weighted sum of the inputs determines the activity of a neuron and is given by (Fahlman , 1990) ⎛ i =exp – ⎝ T n xi i n wji et al STUDY OF RCM-BASED MAINTENANCE PLANNING FOR COMPLEX STRUCTURES net = j ∑w x n ji i (16) i =1 A simple function is now used to provide a mapping from the n-dimensional space of the inputs onto a 1-dimensional space, which is comprised of an output value that a neuron sends to its neighbors The output of a neuron is a function of its activity (17) y = f ( net ) Many types of neural networks have been proposed by changing the network topology, node characteristics, and the learning procedures In this study, we use the backpropagation network, that is, a multi-layer feed-forward neural network topology with one hidden-layer A backpropagation network consists of an input layer, hidden layers, an output layer, and adaptive connections between successive layers Back-propagation networks can learn when presented with input-target output pairs The backpropagation is usually used for “supervised” learning It is essentially a special-purpose steepest-descent algorithm that adjusts the strength of the w connection and other additional internal parameters that are sometimes added to increase the flexibility, i.e to reproduce the output of given input-output training sets within a required error tolerance The training error is defined as follows: (Rumelhart et al., 1986) ji E = sum ∑ E = ∑ ∑ (R n n n p p =1 p =1 k T =1 _ pk –R O _ pk (18) )2 where E is the square error for the p-th training pattern, R _ is the teacher reliability signal for the k-th unit in the output layer and the p-th training pattern, R _ is the output reliability signal for the k-th unit in the output layer and the p-th training pattern, m is the number of output units, and n is the number of patterns In the training process, the connection weights, w , are repeatedly modified based on the steepest descent method in order to minimize the above square error ∂ E - , w =w +∆w (19) ∆w = –η ∂w where η is learning rate constant The training is sensitive to the choices of the various net learning parameters The first parameter is the “learning rate,” which essentially governs the “step size” and is a concept familiar to the optimization community The learning rate constant should be updated according to the following rule p T pk O pk ji sum ji new old ji ji ji ji ⎧ +a if ∆E > consistently ⎪ ∆η = ⎨–b η if ∆E < ⎪ otherwise , ⎩ sum order to aid numerical stability and furthermore, to go over the local minima that are encountered in the search This scheme is implemented by giving a contribution from the previous step time to each weight change: (21) ∆ w ( n ) = – η ∇E ( n ) + α ∆ w ( n – ) where α ∈ [ 0,1 ] is a momentum parameter for which a value of 0.9 is often used The momentum term typically helps to speed up the convergence and to achieve an efficient and more reliable learning profile sum m m 4.2 Evolutionary Algorithm Evolutionary algorithms (EA) are probabilistic optimization algorithms that are based on the model of natural evolution The algorithms have clearly demonstrated their capability to create good approximate solutions to complex optimization problems The popularity of the algorithms is due to the following characteristics: (1) less possibility of converging to a local minimum as the search starts from a number of points, (2) compatibility with the parallel computer, (3) robustness since only the objective function information is required, and (4) the capability to effectively find a solution in a broad search space through probabilistic operations Out of all of the algorithms (such as the genetic algorithms (GAs) (Holland, 1975), evolution strategies (ES’s) (Rechenberg, 1973), and evolutionary programming (EP) (Fogel et al., 1966)), GAs are the most popular because they reproduce processes that only use a few deterministic rules (mostly randomized processes) Thus, GAs can be applied to a variety of complex optimization problems Genetic Algorithms that have been developed by Holland have traditionally used bit-strings of a fixed length, l, i.e u ∈ I = {0,1 } The evaluation of the fitness can be conducted with linear scaling, where the fitness of each individual is calculated as the worst individual of the population subtracted from its objective function value (Goldberg, 1989) Φ( R )=max {ψ ( R )|R ∈ P }−ψ (R ) , ∀i ∈ {1, , λ } (22) k l i k k i i k k k i Φ(R ) ≥ is thus satisfied by this equation Selection in GAs emphasizes a probabilistic survival rule that is mixed with a fitness dependent chance of having different partners for producing more or less offspring Holland identifies a necessity to use proportional selection in order to optimize the trade-off by exploiting promising regions of the search space as well as exploring other regions For proportional selection, the reproduction probabilities of individuals, u , are given by their relative fitness: k i i Φ( R ) p ( R )= λ Φ( R ) k sum (20) This learning rate approach is an adaptive learning constant The second parameter is the “momentum coefficient” that forces the search to continue in the same direction in 639 k s i (23) i ∑ j k j =1 Recombination of the genetics is conducted by the crossover An exogenous parameter p (crossover rate) indicates the probability per individual of undergoing recombination c 640 Y T SON et al Table Maintenance data for the virtual system Part Failure Operation Part Failure Operation Code Number Time (hrs) Code Number Time (hrs) 1 106 4690 158 4839 707 5248 1812 : : : 2194 37 86831 2893 55 86964 3592 39 87276 2 3998 68 87444 4642 56 87471 Table The result of the RGA Part Failure λ Code Number β 39 0.924 0.001072 68 1.037 0.000511 56 1.013 0.000554 37 1.114 0.000118 Failure Rate 0.000452 0.000777 0.000640 0.000431 MTBF 2210.014 1285.856 1561.953 2317.367 Figure RBD model of the virtual complex structure where t denotes the operational time for each device, λ (t) is a function of the failure rate, and m is the average interval of failure, MTBF Using Equation (26), Equation (27) is generated with a transformation to the time domain after solving the reverse function (27) λ To calculate the repairing interval, equation (27) can be re-established with the current failure rate, average life cycle, and the time that is spent on repairs This can satisfy the result level of the optimized reliability of individual parts, which consist of complex structures t = − - loge R = –m loge R * Typical values for pc are in the range of [0.6, 1.0] In the case of one-point crossover, two randomly-selected individuals are renewed by two offspring individuals: k k k k ( ) ( ) (24) Mutations in GAs work on the bit string level and are traditionally referred to as a background operator It works by occasionally inverting single bits of the individuals; the probability, pm, of this event is usually very small ⎧ k+1 k + ⎪ u ij uij = ⎨ k+1 ⎪ – uij ⎩ ϑ ϑ ij > pm ij ≤ pm (25) where ϑ ∈ [ 0,1] is a uniform random variable that is sampled anew for each bit These reproductive operations form one generation of the evolutionary process, which corresponds to one-iteration in the algorithm, and the iteration is repeated until a given terminal criterion is satisfied ij 4.3 Introduction of the Maintenance Cycle The failure in working maintenance randomly occurs at a constant failure rate Thus, the reliability of an individual part can be calculated by the RAMS algorithms (Lee et al., 2003) with exponential distribution by using the failure rate and the MTBF concept It can be expressed by Equation (26) R ( t ) =exp [ –∫ t λ * * i i =1,2, , n (28) where T * is the optimal repairing interval of the i-th device, λ i is the i-th current failure rate, mi is the i-th current MTBF, and Ri is the i-th optimized reliability, respectively i ⎧ u α = { u α , … , u α m , u β m + , … , u βl } ⎨ k+1 k k k k ⎩ u β = { u β1 , … , u βm , u α m + , … , u α l } k+1 Ti = − - loge Ri = − mi loge Ri λ(t)dt]=exp[ –λt]=exp – -tm (26) APPLICATION 5.1 Model Construction This research constructed the virtual complex structure in order to apply the presented methodology to a practical model Figure shows the RBD (Reliability Block Diagram) of the structure An RBD (Wang, 2004) is a graphical presentation of a system diagram in a reliability-wise or functional logic, i.e., connecting subsystems or components according to their function or reliability relationship The model that is composed of four parts has a mixed series-parallel structure The virtual system is constructed by maintenance data that can describe the operation of its system The system’s operational data contains repair data for the four parts that were obtained over a period of ten years (87,600 hrs) and are composed of the failed part code, cumulative failure number, and the operation time Table shows the partial data for the system’s operational data The data is based on two suppositions: first, parts are not exchanged for ten years, and second, if the parts are repaired because of failure, they perfectly recover their function to 100% reliability STUDY OF RCM-BASED MAINTENANCE PLANNING FOR COMPLEX STRUCTURES Figure Historical failure rate MTTR information, which can be obtained from the system’s operational data, is specified as an arbitrary value in order to simplify the problem Accordingly, the maintainability of each part is calculated by Equation (14) by using an arbitrarily specified MTTR 5.2 Reliability Growth Analysis for the Model Table shows the total failure number of the four parts over about ten years (87,471 hrs) and the parameters, failure rate, and MTBF, which are calculated with Equation (1) The failure rates of the four parts are analyzed by applying β and λ to Equation (2) The result is illustrated in Figure Because the failure rates of the four parts are very small (less than 0.001), the values of the scale parameter, λ , for all of the parts are small In addition, it does not have a predominant influence on the historical failure rate of each part The shape parameter, β, for each part is an important index for estimation of the failure rate in the future If this system is operated in the present condition, the failure rate of part one, whose β is less than one, would gradually decrease On the other hand, we can infer that the failure rate of part four would increase and that the failure rates of parts two and three would be nearly linear because their β approaches one Table also shows the estimated failure rate of each part and the MTBF for a period of ten years since the system’s operation The failure rate of part two, which has the most failures among the four parts, is the highest while the MTBF of part two showed a contrary trend to the failure rate trend The estimated failure rate and the MTBF were used to calculate the maintenance cycle that satisfies the system’s target reliability by using the RAMS algorithm This research assumes that the point of ten years (87,471 hrs) is the ‘current time’ In this case, the system failure rate is 0.001235 at the ‘current time’ and its MTBF is 809.61 hrs on the grounds of Equations (1)~(2) Therefore, we predicted the reliability change of the four parts and the system for 809.61 hrs, which is the ‘system’s MTBF’, from 641 Figure Predicted reliability the ‘current time’ Figure graphically shows the result The reliabilities of the four parts and the system decrease when their former maintenance is not included In particular, part four has the lowest reliability at the current time because its former maintenance was carried out the earliest Comparison of the decreasing rate of each part indicates that the slope of the reliability change is influenced by the values of the failure rate and the MTBF This means that the rate of reliability decrease of a part with a small failure rate or a big MTBF is low The relationship between the reliability of each part and the one of the system at the same time is used for training the neural network; the reliabilities of the four parts comprise the input layer while the reliability of the system is the target layer 5.3 Reliability Allocation and Maintenance Cycle The design variables are the reliability of each part, such as Rpart 1, Rpart2, Rpart 3, and Rpart4 The objective function is the cost function for the system’s operation and it also act as the fitness function of the genetic algorithm In constraints, the system’s target reliability Rg is determined to be 0.9 while every Ri,max is 0.999 The Ri,min values are 0.5430, 0.7499, 0.186, and 0.0, respectively In order to complete the cost function, we have to determine the values of the constant The constant values are the weight factor for each cost of the cost function and the maintainability When determining the values of the Figure Three-layer neural network 642 Y T SON et al Table Constant values for application Part Part Part w1i 6.07 2.91 3.73 w2i 2.75 4.95 7.08 w3i 0.43 2.41 1.14 0.31 0.40 0.62 mˆ i Part 4.66 8.39 10 0.87 optimization constants, the maintenance data of the Korean VVVF urban transit brake system is used to obtain realistic constant values Each weight factor that is applied to the optimization is scaled by the following method The biggest value among the weight factors is ‘10’ while the other values are relatively scaled to the biggest one The determined constant values are expressed in Table Figure shows the neural network for the sample model We extracted the reliability data of the system and the four parts at the stated time from the reliability growth graph for 809.61 hrs The number of extracted reliability data, which is called the pattern, was 1,296, according to various operating times The input layer and the target layer are the reliabilities of the four parts and the system reliability, respectively The number of hidden layers that are used in this research was ten Figure shows the three-layer neural network This research trains the neural network for 100,000 iterations using 1,296 patterns, according to various operating times During the training of the neural network, the error rate was 0.001 and the learning rate was 1.05 The neural network training result that is between the target value and the out value is shown in Figure The final SSE (Sum Square Error) of the trained neural network was 0.004804 To verify the training result of the neural network, we compared the output value with the target value from the two patterns that were arbitrarily extracted Table shows the verification result of the neural network training We could verify that the relative error rate between the output value and the target value was a very Figure Neural network training results Table Verification of the trained neural network Part Part Part Part 0.955760 0.925178 0.937984 0.957765 Case Target value=0.911146 Output value=0.911067 Relative error rate=0.0087% Part Part Part Part 0.913477 0.855954 0.879814 0.917315 Case Target value=0.823439 Output value=0.823347 Relative error rate=0.0117% small value, about 0.01%, means that the training result of the neural network was good The genetic algorithm for this research was set up as follows The population size was 100; the crossover rate, pc, was 0.25; and the mutation rate, pm, was 0.01 Each parameter was represented as a 25-bit binary number and the roulette wheel selection method was adopted for the selection We can estimate the system’s reliability from a trained neural network The estimated system reliability is compared with the system’s target reliability in order to calculate the repair cost of the cost function Table shows the converged results of the reliability of each part by using the genetic algorithm We can keep a check on convergence at the 53-rd iteration from the total of 100 iterations The estimated system reliability (0.9002) that was obtained by using the optimized reliability of each part satisfied the system target reliability (0.9) while each reliability of the four parts also satisfied the side constraints Considering the optimal reliabilities of the four parts, we can see the influence of the system’s structure Parts one and four of the series structure show the reliability optimization result Their reliabilities are higher than 0.95 On the otherhand, parts two and three of the parallel structure have lower reliabilities than parts one and two of the series structure, respectively The following result was obtained Although the system’s structures were the same, their reliabilities were different Table Results of the reliability allocation and the maintenance cycle for RCM Part Maintenance Code Normal Optimal Cycle (hrs) Remark 0.95 0.95268 107.14 0.95 0.85554 200.62 System target reliability 0.95 0.92373 123.92 =0.90 0.95 0.95559 105.28 Rs 0.8999 0.9002 Feasible and active Cost 43.7381 38.7106 Decrease 12.98% cost STUDY OF RCM-BASED MAINTENANCE PLANNING FOR COMPLEX STRUCTURES because of the cost weight factors and the maintainability of each part This means that the operation characteristics of each part influence the reliability optimization In particular, in an example model, the maintenance cost factor has the largest influence This research considers the ‘virtual system’ in order to verify the reliability optimization result The case of the virtual system is same as the example model but it maintains all of the parts in the same condition without reliability allocation The reliability of each part in a virtual system is 0.95, which satisfies the system’s reliability of 0.9 (90 %), and the value of the objective function (system operation cost function) is equal to 43.74 When this value is compared with the optimization value (43.74), we can see that 12.98% of the operational cost is saved by the reliability optimization We can acquire the optimal maintenance cycle of each part by substituting the optimized reliability and the current failure rate that was estimated from the reliability growth analysis by using Equation (28) The result of the maintenance cycle optimization is shown in Table CONCLUSION This research presented methodology for acquiring a proper maintenance plan for individual parts in a complex structure The key concept of this methodology is the optimization of the RCM-based maintenance cycle In order to construct an RCM-based optimization problem, the indices of the reliability and maintainability in a system are required This information can be obtained from historical maintenance data Indices can be estimated in terms of the failure rate, MTBF, and MTTR In particular, we applied the reliability growth analysis by using the AMSAA model to obtain the reliability indices Reliability growth analysis can also estimate the failure rate and MTBF The objective of this research is to obtain the total cost function for operating a complex structure The total cost function is influenced by the reliabilities of the individual parts, which are designated as design variables The reliabilities of the individual parts and the system are restricted by constraints The cost function consists of three parts: initial, repair, and maintenance cost functions The cost of each cost function is essential for building and maintaining the system All of the cost functions have the weight factors that reflect the characteristics of the individual parts Therefore, these cost functions can be applied to the system’s operational condition We introduced the Neuro-Evolutionary technique as the optimization method The combination of the neural network and the evolutionary algorithm constitute this optimization technique, which is advantageous for solving nonlinear and complex optimization problems In the optimization process, the neural network derives the reliability relationship between the individual parts in a structure while the evolutionary algorithm optimizes the reliabilities of the individual 643 parts by using the trained neural network Finally, this research established the proper maintenance plan with the life cycles of the individual parts that are derived from the optimal reliability and reliability indices by using the inverse analysis of the fundamental reliability function In order to verify the utility of the research, we applied the presented method to the virtual complex structure as a sample model The reliabilities of the four parts in the sample model were optimized by considering the system’s structure We can see that the operation cost is saved by comparing the optimization result and the initial value This savings means that the maintenance work was uniformly performed and does not reflect either the system’s operational condition or the characteristics of the individual parts Finally, we obtained the optimal life cycles of the individual parts in the sample model, as shown in Table In conclusion, this research presented a useful methodology for obtaining a maintenance plan by considering the safety from both the reliability analysis and the economical efficiency The two factors of safety and the economy efficiency are essential conditions when constructing and maintaining a system, and their importance is increasing in modern complex systems Therefore, the presented method for obtaining a maintenance plan will be an available tool in the design stage of systems operation ACKNOWLEDGEMENT−This work was supported by the second Brain Korea 21 project and Research & Development on the Standardization of Urban Railway 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