3.2 Theoretical Overview of Reliability and Performance in Engineering Design 53 Fig. 3.6 Power train system reliability of a haul truck (Komatsu Corp., Japan) Fig. 3.7 Power train system diagram of a haul truck 54 3 Reliability and Performance in Engineering Design Table 3.2 Power train system reliability of a haul truck Output shaft assembly Transmission sub-system Power train system No. of components 5 50 100 Group reliability 0.99995 0.99950 0.99900 Output shaft assembly reliability =(0.99999) 5 = 0.99995 Transmission sub-system reliability =(0.99999) 50 = 0.99950 Power train system reliability =(0.99999) 100 = 0.99900 components are considered to have the same reliability of 0.99999. The reliability calculations are given in Table 3.2. The series formula of reliability implies that the reliability of a group of series components is the product of the reliabilities of the individual components. If the output shaft assembly had five components in series, then the output shaft assem- bly reliability would be five times the product of 0.99999 = 0.99995. If the torque converter and transmission assemblies had a total of 50 different components, be- longing to both assemblies all in series, then this sub-system reliability would be 50 times the product of 0.99999 = 0.99950. If the power train system had a total of 100 different components, belonging to different assemblies, some of which belong to different sub-systems all in series, then the power train system’s reliability would be a 100 times the product of 0.99999 = 0.99900. The value of a component reliability of 0.99999 implies that out of 100,000 events, 99,999 successes can be expected. This is somewhat cumbersome to en- visage and, therefore, it is actually more convenient to illustrate reliability through its converse, unreliability. This unreliability is basically defined as Unreliability = 1−Reliability . Thus, if component reliability is 0.99999, the unreliability is 0.00001. Th is implies that only one failure out of a total of 100,000 events can be expected. In the case of the haul truck, an event is when the component is used under gearshift load stress every haul cycle. I f a haul cycle was an average o f 15 min, then this would imply that a power train component would fail about every 25,000 operational hours. The output shaft assembly reliability of 0.99995 implies that only five failures out of a total of 100,000 events can be expected, or one failure every 20,000 events (i.e. haul cycles). (This means one assembly failure every 20,000 haul cycles, or every 5,000 operational hours.) A sub-system (power converter and transmission) relia- bility of 0.99950 implies that 50 failures can be expected out of a total of 100,000 events (i.e. haul cycles). (This means one sub-system failure every 2 ,000 haul cy- cles, or every 500 operational hours.) Finally, the power train system reliability of 0.99900 implies that 100 failures can be expected out of a total of 100,000 events (i.e. haul shifts). (This means one system failure every 1,000 haul cycles, or every 250 operational hours!) Note how the reliability decreases from a component reli- ability of only one failure in 100,000 events, or every 25,000 operational hours, to the eventual system reliability, which has 100 components in series, with 100 fail- 3.2 Theoretical Overview of Reliability and Performance in Engineering Design 55 Single component reliability 1.00 0 0.2 0.4 0.6 0.8 1 1.2 0.98 0.96 0.94 0.92 0.9 0.88 0.86 N = 10 N = 20 N = 50 N = 100 N = 300 Reliability of N series components Fig. 3.8 Reliability of groups of series components ures occurring in a total of 100,000 events, or an average of one failure every 1,000 events, or every 250 operational hours. This decrease in system reliability is even more pronouncedfor lower component reliabilities. For example, with identical component relia bilities of 0.90 (in other words, one expected failure out of ten events), the reliability of the power train system with 100 components in series would be practically zero! R System =(0.90) 100 ≈ 0 . The following Fig. 3.8 is a graphical portrayal of how the reliability of groups of series componentschangesfor different valuesof individualcomponentreliabilities, where the reliability of each component is identical. This graph illustrates how close to the reliability value of 1 (almost 0 failures) a component’s reliability would have to be in order to achieve high group reliability, when there are increasingly more components in the group. The effect o f redundancy in system reliability When very high system reliabili- ties are required, the designer or m anufacturer must often duplicate components or assemblies, and sometimes even whole sub-systems, to meet the overall system or equipment reliability goals. I n systems or equipment such as these, the components are said to be redundant, or in parallel. Just as the reliability of a group of series components decreases as the number of components increases, so the opposite is true for redundant or parallel components. Redundant components can dramatically increase the reliability of a system. How- ever, this increase in reliability is at the expense o f factors such as weight, space, and manufacturing and maintenance costs. When redundant components are being analysed, the term unreliability is preferably used. This is because the calculations 56 3 Reliability and Performance in Engineering Design Component No.1 Reliability R1 = 0.90 Component No.2 Reliability R2 = 0.85 Fig. 3.9 Example of two parallel components are easier to perform using the unreliability of a component. As a specific example, consider the two parallel components illustrated below in Fig. 3.9, with reliabilities of 0.9 and 0.85 respectively Unreliability: U =(1−R1)×(1−R2) =(0.1) ×(0.15) = 0.015 Reliability of group: R = 1 −Unreliability = 1−0.015 = 0.985. With the individual component reliabilities of only 0.9 (i.e. ten failures out of 100 events), and of 0.85 (i.e. 15 failures out of 100 events), the overall system re- liability of these two components in parallel is increased to 0.985 (or 1 5 failures in 1,000 events). The improvement in reliability achieved by components in paral- lel can be further illustrated by referring to the graphic por trayal below (Fig. 3.10 ). These curves show how the reliability of groups of parallel components changes for different values of individual component reliabilities. From these graphs it is obvious that a significant increase in system reliability is obtained from redundancy. To cite a few examples from these graphs, if the reliability of one component is 0.9, then the reliability of two such components in parallel is 0.99. The reliability of three such components in parallel is 0.999. This means that, on average, only one system failure can be expected to occur out of a total of 1,000 events. Put in more correct terms, only one time out of a thousand will all three components fail in their function, and thus result in system functional failure. Consider now an example of series and parallel assemblies in an engineered in- stallation, such as the slurry mill illustra ted below in Fig. 3.11. The system is shown with some major sub-systems. Table 3.3 gives reliability values for some of the critical assemblies and components. Consider the overall reliability of these sub- 3.2 Theoretical Overview of Reliability and Performance in Engineering Design 57 0 0.2 0.4 0.6 0.8 1 10.90.80.70.60.50.40.30.20.10.00 1.2 N = 5 N = 3 N = 2 Single component reliability Reliability of N parallel components Fig. 3.10 Reliability of groups of parallel components Fig. 3.11 Slurry mill engineered installation 58 3 Reliability and Performance in Engineering Design Table 3.3 Component and assembly reliabilities and system reliability of slurry mill engineered installation Components Reliability Mill trunnion Slurrying mill trunnion shell 0.980 Trunnion dri ve gears 0.975 Trunnion dri ve gears lube (×2 units) 0.975 Mill drive Drive motor 0.980 Drive gearbox 0.980 Drive gearbox lube 0.975 Driv e gearbox heat exchanger (×2 units) 0.980 Slurry feed and screen Classification feed hopper 0.975 Feed hopper feeder 0.980 Feed hopper feeder motor 0.980 Classification screen 0.950 Distribution pumps Classification underflow pumps (×2 units) 0.980 Underflow pumps motors 0.980 Rejects handling Rejects con veyor feed chute 0. 975 Rejects conve yor 0.950 Rejects conveyor drive 0.980 Sub-systems/assemblies Slurry mill trunnion 0.955 Slurry mill drive 0.935 Classification 0.890 Slurry distri bution 0.979 Rejects handling 0.908 Slurry mill system Slurry mill 0.706 systems once all of the parallel assemblies and components have been reduced to a series configuration, similar to Figs. 3.4 and 3.5. Some of the major sub-systems, together with their major components, are the slurry mill trunnion, the slurry mill drive, classification, slurry distribution, and re- jects handling. The systems hierarchy of the slurry mill first needs to be identified in a top-level systems–assembly configuration, and acco rdingly is simply structured for illustra- tion purposes: 3.2 Theoretical Overview of Reliability and Performance in Engineering Design 59 Systems Assemblies Milling Slurry mill trunnion Slurry mill drive Classification Slurry feed Slurry screen Distribution Slurry distribution pumps Rejects handling Slurry mill trunnion: Trunnion shell×Trunnion drive gears×Gears lube (2 units) =(0.980×0.975) ×[(0.975+ 0.975) −(0.975 ×0.975)] =(0.980×0.975 ×0.999) = 0.955 , Slurry mill drive: Motor×Gearbox×Gearbox lube×Heat exchangers (2 units) =(0.980×0.980 ×0.975) ×[(0.980+ 0.980) −(0.980×0.980)] =(0.980×0.980 ×0.975×0.999) = 0.935 , Classification: Feed hopper×Feeder×Feeder motor×Classification screen =(0.975×0.980 ×0.980×0.950) = 0.890 , Slurry distribution: Underflow pumps (2 units)×Underflow pumps motors =[(0.980 + 0.980) −(0.980×0.980)] ×0.980 =(0.999×0.980) = 0.979 , Rejects handling: Feed chute×Rejects conveyor×Rejects conveyor drive =(0.975×0.950 ×0.980) = 0.908 , Slurry mill system: =(0.955×0.935 ×0.890×0.979×0.908) = 0.706 . 60 3 Reliability and Performance in Engineering Design The slurry mill system reliability of 0.706 implies that 294 failures out of a total of 1,000 events (i.e. mill charges) can be expected. If a mill charge is estimated to last for 3 .5 h, this would mean one system failure every 3.4 charges, or about every 12 operational hours! The staggering frequency of one expected failure every operational shift of 12 h, irrespective of the relatively high reliabilities of the system’s components, has a sig- nificant impact on the approachto systems design for integrity (reliability, availabil- ity and maintainability), as well as on a proposed maintenance strategy. 3.2.1 Theoretical Overview of Reliability and Performance Prediction in Conceptual Design Reliability and performance p rediction attempts to estimate the probability of suc- cessful performance of systems. Reliability and perfor mance prediction in this con- text is considered in the conceptual design phase of the engineering design process. The most applicable methodology for reliability and performance prediction in the conceptual desig n phase in cludes basic concepts of mathematical modelling such as: • Total cost models for design reliability. • Interference theory and reliability modelling. • System reliability modelling based on system per formance. 3.2.1.1 Total Cost Models for Design Reliability In a paper titled ‘Safety and risk’ (Wolfram 1993), reliability and risk prediction is considered in determining the total potential cost of an engineeringproject. With in- creased design reliability (including strength and safety), project costs can incr ease exponentially to some cut-off point. The tendency would thus be to achieve an ‘ac- ceptable’ design at the least cost possible. a) Risk Cost Estimation The total potential cost of an engineering project compared to its design reliability, whereby a minimumcost point designatedthe economic optimum reliability is deter- mined, is illustrated in Fig. 3.12. Curve ACB is the normal ‘first cost curve’, which includes capital costs plus operating and maintenance costs. With the inclusion of the ‘risk cost curve’ (CD), the effect on total project cost is reflected as a concave or parabolic curve. Thus, designs of low reliab ility are not worth consideration because the risk cost is too high. 3.2 Theoretical Overview of Reliability and Performance in Engineering Design 61 C B First cost curve Apparent economic optimum reliability Risk cost curve (Capital costs plus operating and maintenance costs) Increased risk of failure Strength, safety and reliability First cost Risk cost D A C O S T DESIGN RELIABILITY * Fig. 3.12 Total cost versus design reliability The difference between the ‘risk cost curve’ and the ‘first cost curve’ in Fig. 3.12 designates this risk cost, which is a function o f the pr obability and consequences of systems failure on the project. Thus, the risk cost can be formulated as Risk cost = Probability of failure×Consequence of failure. This probability and consequence of systems failure is related to process reliability and criticality at the higher systems levels (i.e. process and system level) that is established in the design’s systems hierarchy,or systems b reakdown structure (SBS). According to Wolfram, there would thus appear to be an economically optimum level of process r eliability (and safety). However, this is misleading, as the predic- tion of processreliability and the inherentprobabilityof failure do not reflect reality precisely, and the extent of the error involved is uncertain. In the face of this un- certainty, there is the tendency either to be conservative and move towards higher predicted levels of design reliability, or to rely on previous designs where the in- dividual process systems on their own were adequately designed and constructed. In th e first case, this is the same as selecting larger safety factors when there is ignorance about how a system or structure will behave. In the latter case, the combi- nation and integration of many previously designed systems inevitably give rise to design complexity and consequent frequent failure, where high risks of the integrity of the design are encountered. Consequently, there is a need to develop good design models that can reflect re- ality as closely a s possible. Furthermore, Prof. Wolfram contends that these design models need not attempt to explain wide-ranging phenomena, just the criteria rele- vant to the design. However, the fact that engineering design should be more precise 62 3 Reliability and Performance in Engineering Design close to those areas where failure is more likely to occur is overlooked by most de- sign engineers in the early stages of the design process. The questions to be asked then are: which areas are more likely to incur failure, and what would the probabil- ity of that likelihood be? The penalty for this uncertainty is a substantial increase in first costs if the project economics are feasible, or a high risk in the consequential risk costs. b) Project Cost Estimation Nearly every engineering design project will include some form of first cost estimat- ing. This initial cost estimating may be performed by specific engineeringpersonnel or by separate cost estimators. Occasionally, other resources, such as vendors, will be required to assist in first cost estimating. The engineering d esign project manager determines the need for cost estimatin g services and making arrangements for the appropriate services at the ap propriate times. Ordinarily, cost estimating services should be obtained from cost estimators employed by the design engineer. First cost estimating is normally done as early as possible, when planning and scheduling the project, as well as finalising the estimating approach and nature of engineering input to be used as the basis for the cost estimate. Typesoffirstcostestimates First cost estimates consist basically of investment or capital costs, operating costs, and maintenance costs. These types of estimates can be evaluated in a number of ways to suit the needs of the project: • Discounted cash flow (DCF) • Return on investment (ROI) • Internal rate of return (IRR) • Sensitivity evaluations Levels of cost estimates The most important consideration in planning cost esti- mating tasks is the establishment of a clear understanding as to the required level or accuracy of the cost estimate. Basically, each level of the engineering design process has a corresponding level of cost estimating, whereby first cost estimations are usually performed during the conceptual and preliminary design phases. The following cost estimate accuracies for each engineering design phase are considered typical: • Conceptual design phase: plus or minus 30% • Preliminary design phase: plus or minus 20% • Final detail design phase: plus or minus 10% The percentages imply that the estimate will be above or below the final construc- tion costs of the engineered installation, by that amount. Conceptual or first cost estimates are generally used for project feasibility, initial cash flow, and funding purposes by the client. Preliminary estimates that inclu de risk costs are used for ‘go-no-go’ decisions by the client. Final estimates are used for control purposes during procurement and construction of the final design. . Slurry mill engineered installation 58 3 Reliability and Performance in Engineering Design Table 3.3 Component and assembly reliabilities and system reliability of slurry mill engineered installation Components. Design Reliability In a paper titled Safety and risk’ (Wolfram 1993), reliability and risk prediction is considered in determining the total potential cost of an engineeringproject. With in- creased. fail- 3.2 Theoretical Overview of Reliability and Performance in Engineering Design 55 Single component reliability 1.00 0 0.2 0.4 0.6 0 .8 1 1.2 0. 98 0.96 0.94 0.92 0.9 0 .88 0 .86 N = 10 N = 20 N = 50 N