Koo et al 2009

Automated Impedance-based Structural Health Monitoring Incorporating Effective Frequency Shift for Compensating Temperature Effects KI-YOUNG KOO,1 SEUNGHEE PARK,2,* JONG-JAE LEE3 AND CHUNG-BANG YUN1,* Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology Guseong-dong, Yuseong-gu, Daejeon, Korea Department of Civil and Environmental Engineering, Sungkyunkwan University, Cheoncheon-dong, Jangan-gu Suwon, Gyeonggi-do, Korea Department of Civil and Environmental Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul, Korea ABSTRACT: This study presents an impedance-based structural health monitoring (SHM) technique considering temperature effects The temperature variation results in significant impedance variations, particularly a frequency shift in the impedance, which may lead to erroneous diagnostic results of real structures, such as civil, mechanical, and aerospace structures In order to minimize the effect of the temperature variation on the impedance measurements, a previously proposed temperature compensation technique based on the cross-correlation between the reference-impedance data and a concurrent impedance data is revisited In this study, cross-correlation coefficient (CC ) after an effective frequency shift (EFS), which is defined as the frequency shift causing two impedance data to have the maximum correlation, is utilized To promote a practical use of the proposed SHM strategy, an automated continuous monitoring framework using MATLABÕ is developed and incorporated with the current hardware system Validation of the proposed technique is carried out on a lab-sized steel truss bridge member under a temperature varying environment It has been found that the CC values have shown significant fluctuations due to the temperature variation, even after applying the EFS method Therefore, an outlier analysis providing the optimal decision limits under the inevitable variations has been carried out for more systematic damage detection It has been found that the threshold level shall be properly selected considering the daily temperature range and the minimum target damage level for detection It has been demonstrated that the proposed strategy combining the EFS and the outlier analysis can be effectively used in the automated continuous SHM of critical structural members under temperature variations Key Words: impedance of piezoelectric sensors, structural health monitoring, temperature effects, effective frequency shift, cross-correlation coefficients, outlier analysis, steel truss members INTRODUCTION health monitoring (SHM) has become an important issue in many fields, such as civil, mechanical, and aerospace engineering In recent years, the electromechanical impedance method, which utilizes piezoelectric materials as collocated actuator-sensors, has emerged as a new SHM technique (Giurgiutiu and Rogers, 1997; Giurgiutiu et al., 1999; Park et al., 2000, 2003a, 2005, 2006a; Soh et al., 2000; Tseng et al., 2000; Zagrai and Giurgiutiu, 2001; Bhalla et al., 2002) In this technique, a piezoelectric sensor is surface-mounted to the host structure by means of a high strength epoxy S TRUCTURAL *Authors to whom correspondence should be addressed E-mail: ycb@kaist.ac.kr and shparkpc@skku.edu Figures 1–18 appear in color online: http://jim.sagepub.com JOURNAL OF INTELLIGENT adhesive and its electrical impedance is extracted across a high frequency-band, typically in the order of kHz The real part of this signature is used as a representation of the local dynamic parameters of the structure in the vicinity of the sensor Damage to the structure in the vicinity of the sensor is expected to alter this signature thereby giving an indication of the imminent damage However, there are many impediments to the practical application of the technique for SHM of real structures, such as bridges, buildings, and airplanes The main challenge lies in achieving continuous monitoring of the impedance response of the piezoelectric sensor over sufficiently long periods; several days, months, or years To this end, the development in terms of hardware and software systems has been pursued From the viewpoint of hardware systems, the low-cost, portable, and wireless MATERIAL SYSTEMS AND STRUCTURES, Vol 20—March 2009 1045-389X/09/04 0367–11 $10.00/0 DOI: 10.1177/1045389X08088664 ß SAGE Publications 2009 Los Angeles, London, New Delhi and Singapore 367 368 K.-Y KOO ET AL telemetry requirements resulted in an on-board active sensor system (Grisso and Inman, 2005; Mascarenas et al., 2006; Park et al., 2006b) The on-board active sensor system interrogates a structure utilizing a selfsensing macro-fiber composite (MFC) patch and the low-cost impedance measuring chip, and all the structural interrogation and data analysis are pursued in near real-time at the sensor location From the viewpoint of software systems, the development of an automated algorithm suitable for continuous monitoring under significant environmental variation especially temperature effects should be accompanied with the corresponding hardware system Several studies have been reported about the temperature variation effects on the impedance measurement (Sun et al., 1995; Park et al., 1999a,b; Bhalla et al., 2003) Sun et al (1995) used a temperature compensation method based on crosscorrelation to correct the horizontal shift in the impedance signature pattern Park et al (1999a,b) proposed an impedance-based health monitoring technique under a temperature varying environment considering the root mean square deviations (RMSD) of the measured signatures after introducing proper shifts in the horizontal and vertical directions Bhalla et al (2003) also investigated the influence of the structure–actuator interactions and temperature on the impedance signatures In this study, the change of the impedance data under temperature variation has been investigated using an automated continuous monitoring system In order to minimize the effect of the temperature variation on the impedance measurements, the temperature compensation technique previously proposed by Park et al (1999a) is revisited In this study, the authors utilize cross-correlation coefficient (CC) with an effective frequency shift (EFS) which is defined as the frequency shift causing the concurrent impedance data to have the maximum correlation with the reference-impedance data The proposed technique was applied to health monitoring of a lab-sized steel truss bridge member under a temperature varying environment It has been found that the CC values have shown significant fluctuations due to the temperature variations, even after applying the EFS method Therefore, an outlier analysis providing the optimal decision limits under this inevitable variation has been carried out for more systematic damage detection Herein, as the damage level increases, the threshold level should be properly selected considering the daily temperature range and the minimum target damage level for detection, and continuous updating of the threshold level is inevitably required Through an experimental study using a MFC sensor to detect artificial cuts on a steel truss member, it has been demonstrated that the proposed strategy combining the EFS method and the outlier analysis can be applied to the automated continuous SHM of critical structural members under temperature variations IMPEDANCE-BASED STRUCTURAL HEALTH MONITORING In general, the impedance-based SHM technique utilizes small piezoelectric sensors, such as piezoelectric ceramic (PZT) and MFC sensors, attached to a structure as self-sensing actuators to simultaneously excite the structure with high-frequency excitations and to monitor the changes in the measured impedance signature Since the piezoelectric sensor is bonded directly to the structure of interest, the mechanical impedance of the structure is directly correlated with the measured electrical impedance of the piezoelectric sensor Figure presents an idealized 1-D model between the piezoelectric sensor and a host structure Then, the electromechanical impedance function of the coupled system can be represented as a function of frequency as (Liang et al., 1994):   Ztotal !ị ẳ i!C  231 1 Zs !ị ZA !ị ỵ Zs !ị 1ị where C is the zero-load capacitance of the piezoelectric sensor, 31 is the electromechanical coupling coefficient of the piezoelectric sensor, ZS is the impedance of the host structure, and ZA is the impedance of the un-bonded piezoelectric sensor Thus, by observing changes in the electrical impedance measurement of the piezoelectric sensor, assessments can be made about the integrity of the host structure (Giurgiutiu and Rogers, 1997; Giurgiutiu et al., 1999; Park et al., 2000, 2003a, 2005, 2006a; Soh et al., 2000; Tseng et al., 2000; Zagrai and Giurgiutiu, 2001; Bhalla et al., 2002) However, there are still many impediments to the practical application of the technique for SHM of real bridge and building structures, such as the sensitivity of the impedance measurement to the temperature variation CONTINUOUS IMPEDANCE MONITORING SYSTEM In recent years, the use of wireless sensors and networks is becoming increasingly popular as a research I = i sin(wt+f) V = n sin(wt) Piezosensor M K C Figure Idealized 1-D electromechanical modeling between a piezoelectric sensor and a host structure (Giurgiutiu and Rogers, 1997) 369 Impedance-based SHM Technique topic for SHM system In particular, development of a self-contained wireless sensor incorporating on-board actuating/sensing, power generation, on-board data processing/damage diagnostic, and radio frequency (RF) technologies is strongly required With the current trend of SHM heading towards unobtrusive selfcontained sensors, the approaches integrating MEMS and RF telemetry-based active sensing systems on the electromechanical impedance-based damage detection technique have been investigated untiringly by several researchers (Grisso and Inman, 2005; Mascarenas et al., 2006; Park et al., 2006b) Grisso and Inman (2005) developed an autonomous on-board wireless impedance-based SHM system The on-board sensor system interrogates a structure utilizing a PZT patch and the low-cost impedance method, and all the structural interrogation and data analysis are pursued in near real-time at the sensor location Moreover, a wireless telemetry that alerts the end user of any harmful changes in the structure is combined Conventional impedance analyzers, such as HP4294A for the electromechanical impedance method are too expensive and too bulky, which is not attractive for real world applications To overcome these limitations, Mascarenas et al (2006) devised an active sensor node, as displayed in Figure 2, which consists of AD5933, a microcontroller (ATmega128L), and a radio frequency (RF) transmitter (XBee) AD5933 developed by Analog Device is a new impedance measuring device of low cost, portable, and readily combined with a wireless telemetry, as shown in Figure 3, which costs only 150$ A PZT patch interrogates a host structure by using a self-sensing technique of the AD5933 All the processes including structural interrogation, data acquisition, signal processing, and damage diagnostic are performed at the sensor location by the microcontroller And only damage diagnostic result implying ‘damage’ or ‘no damage’ will be transmitted to the end-user through the RF data transmission Finally, the LED light shows ‘green’ or ‘red’ color according to ‘intact’ or ‘damage’ state, respectively P Z T Figure A miniaturized impedance measuring device (AD5933) LED AD5933 Microcontroller Tx (RF) Structure Park et al (2006b) validated the feasibility of the active sensing node through two kinds of example studies for corrosion detection on an aluminum beam and loose bolt inspection on a bolt-jointed structure In this context, this study presents an automated continuous impedance monitoring system As a current laboratory test setup, commercial equipments including impedance analyzers and temperature measurement systems are supported via General Purpose Interface Bus (GPIB) which is the most common interface for measurement and control systems, and RS232C which is a standard for serial binary data communication The impedance analyzer HP4294A is connected to a laptop computer through local area network (LAN), so that GPIB commands may be instructed and measurements may be received via telnet protocol A computer program developed in MATLABÕ language performs all the tasks of the continuous impedance monitoring: (1) scheduling and execution of each measurement task, (2) measuring the impedance and temperature through equipment automations, (3) displaying real time measurements and diagnosis results on the screen, and Indoor: 30 m Outoor: 100 m 2.4GHz data link Base station Rx (RF) Active sensing node (MFC + AD5933 + ATMega128L + XBee) Wireless sensor Network system On-line wireless SHM Figure An active sensor node for wireless impedance-based SHM system (Mascarenas et al., 2006) 370 K.-Y KOO ET AL (4) data archiving Using the current continuous impedance monitoring system, the impedance measurement can be carried out automatically over sufficiently long periods EXPERIMENTAL INVESTIGATION Test Specimen and Test Setup An experimental study was carried out to investigate the feasibility of the proposed method for continuous health monitoring using a MFC sensor on a steel truss member under a temperature varying environment The test specimen is a 1/8 scale model with a dimension of 150  150  530 mm3 for a vertical truss member of Seongsu Bridge, Seoul, Korea, which caused the collapse of the bridge in 1994 The specimen consists of two segments with wide flange sections of different flange thicknesses of and mm welded together as in Figure A ‘d33-type’ MFC sensor of 28  14  0.02 mm3 was employed to detect three damage cases with an artificial cut with different lengths of 2, 4, and mm sequentially inflicted at the same location on the welded zone of the specimen The MFC sensor was placed at a distance of 40 mm away from the cut on the outside surface of a flange A thermocouple was also placed near the MFC sensor for temperature measurement The present experimental setup for the impedance-based SHM consists of a host structure, a MFC sensor, an impedance analyzer (HP4294A), a thermocouple, and a laptop computer equipped with the continuous impedance monitoring framework as shown in Figures and Impedance Variations due to Temperature Effects Temperature effects on the impedance signature of the MFC sensor were investigated Figure 6(a) shows the measured impedance data on the intact structure during a period over 10 days The temperature varied in a range Labtop computer Impedance analyzer (HP4294A) LAN connection Telnet protocol to send GPIB commands and to receive measurements Temperature measurement (TC-31K) RS232C Send commands and receive measurement Test specimen A continuous SHM program developed using MATLAB® (1) Scheduled excution (2) Automated measurement (3) Displaying real-time results (4) Data archiving Figure An automated continuous impedance monitoring system 50 mm mm MFC Thermocoupler 530 mm Cut MFC (28x14x0.02 mm3) Thermocoupler 40 mm Cut mm 144 mm Figure Test specimen, MFC sensor, and thermocouple 150 mm 371 Impedance-based SHM Technique Effective Frequency Shift by Correlation Analysis intact cases) measured at 22.6 and 10.38C, respectively Considerable variation with both vertical and horizontal shifts can be observed between two impedance signatures and the CC is found to be very small as 0.099 To compensate the impedance variation due to the temperature change of 12.38C, an EFS by the crosscorrelation analysis is introduced in this study Herein, ~ for an impedance data y(!) is defined as the the EFS (!) shift corresponding to the maximum cross-correlation with the reference impedance data x(!) as: n o P  i ð!i  !Þ  ~  yị 1=N N iẳ1 x!i ị  xịy max CC ẳ max !~ !~ X Y 2ị As mentioned earlier, the impedance and temperature measurements were carried out continuously during a long period The total number of the measurements for the baseline (intact) state is 700 Figure 7(a) shows the first and the 338th impedance signatures (both for the where x and y are the mean values of two impedance signatures of x(!) and y(!); and  X and  Y are the standard deviations Note that the EFS method may compensate the vertical shifts as well by subtracting the mean values from the original signatures Figure 7(b) shows the normalized impedance signature x^ 338 ð!Þ of of 10.3–26.08C during the period Figure 6(b) shows similar results for a damage case with a cut of mm in the middle of the welded zone of a flange, where the temperature variation was in the range of 15.9–31.38C The results show that the temperature variations caused significant variations in the impedance signatures in both the vertical and the horizontal axes Thus, the impedance changes due to the temperature variations could lead to erroneous diagnostic results about the integrity of the structure Therefore, a damage-feature selection strategy robust to the ambient temperature variation is required in the impedance-based SHM for real applications 350 350 300 300 Real (Z (w)) (b) 400 Real (Z (w)) (a) 400 250 200 250 200 150 150 100 100 50 3.05 3.1 Frequency (Hz) 50 3.05 3.15 × 104 3.1 Frequency (Hz) 3.15 × 104 Figure Impedance variations due to temperature variations in a range of 10.3–31.38C (a) Intact cases, (b) Damage cases with a mm cut Test#1 at 22.6°C Test#338 at 10.3°C (a) 140 CC = −0.099 130 120 110 Test#1 at 22.6°C Test#338 at 10.3°C Max CC = 0.986 Real (Z (w)) Real (Z (w)) (b) 100 90 3.05 3.1 Frequency (Hz) 3.15 × 104 −2 3.05 3.1 3.15 Frequency (Hz) × 104 Figure Impedance data for two intact measurements (a) Original impedance signatures, (b) Normalized impedance signatures after EFS (Reference: Test #1) 372 K.-Y KOO ET AL Test #338 after an EFS along with the normalized reference signature x^ ð!Þ of Test #1 as: ~  x Þ ðx1 ð!  !Þ X1 X338 ~  x 338 Þ ðx338 ð!  !ị : x^ 338 !ị ẳ X1 X338 Cross-correlation-based Damage Detection using Effective Frequency Shift x^ !ị ẳ ð3Þ Excellent match between two signatures for the intact case can be observed, and the maximum correlation coefficient between two signatures after the EFS is found to be as high as 0.986 Figure 8(a) shows two impedance signatures for the same damage case with a cut of mm, measured at 25.88C (Test #763) and 20.28C (Test #805) In the present case, the EFS was evaluated by taking the signature of Test #763 as the reference The maximum CC between two measurements increases from 0.139 to 0.983 as the EFS introduces as in Figure 8(b) Figures and 10 show similar results for damage cases with a cut of and mm, respectively (a) Test#763 at 25.8°C The feasibility of the cross-correlation-based damage detection method using the proposed EFS was investigated for three damage cases with an artificial cut with different lengths of 2, 4, and mm Narrow cuts with a width of 0.5 mm were sequentially inflicted in the middle of the welded zone of a flange as shown in Figure After a cut of mm was inflicted, a series of impedance measurements was carried out under temperature variations in the range of 16.1–28.58C Typically, an impedance measurement at 22.68C (Test #951) for a case with a mm cut is compared with the baseline (intact) impedance measurement at the same temperature (Test #1) in Figure 11(a) A big difference can be observed between two impedance signatures, and the CC values Test#763 at 25.8°C (b) Test#805 at 20.2°C 160 Test#805 at 20.2°C CC = −0.139 140 130 Max CC = 0.983 Real (Z (w)) 150 Real (Z (w)) It has been found that the maximum CC for the same damage case increases remarkably after an EFS 120 110 3.05 3.1 Frequency (Hz) −2 3.05 3.15 × 104 3.1 Frequency (Hz) 3.15 × 104 Figure Impedance data for two damage cases with a mm cut (a) Original impedance signatures, (b) Normalized impedance signatures after EFS (Reference: Test #763) (a) 190 Test#1116 at 22.6°C 180 Test#1155 at 31.3°C 170 CC = −0.100 Test#1116 at 22.6°C (b) Test#1155 at 31.3°C Max CC = 0.967 Real (Z (w)) Real (Z (w)) 160 150 140 130 120 110 100 90 3.05 3.1 Frequency (Hz) 3.15 × 104 −2 3.05 3.1 Frequency (Hz) 3.15 × 104 Figure Impedance data for two damage cases with a mm cut (a) Original impedance signatures, (b) Normalized impedance signatures after EFS (Reference: Test #1116) 373 Impedance-based SHM Technique obtained are as low as 0.055 Figure 11(b) shows the normalized impedance signature of Test #951 after an EFS along with the normalized reference signature (Test #1), which indicates that the maximum correlation coefficient increased remarkably to 0.920 However, the value is smaller than the maximum CC for two intact cases (i.e., 0.986) shown in Figure 7(b) Figures 12 and 13 show similar results for larger damage cases with a and mm cut, respectively The maximum CCs with the intact case (Test #1) after the EFS are found to be 0.851 (for a mm cut) and 0.680 (for an mm cut), which are significantly smaller than the previous intact and small damage cases Figure 14(a) shows the CCs after the EFS for all measurement cases with a cut of 2, 4, and mm The EFSs were evaluated by taking the signature of an intact case (Test #1) as the reference It can be clearly observed that the maximum correlation coefficient after the EFS drops very rapidly with increasing damage level, Test #1466 at 28.4°C Test #1522 at 21.7°C (a) 350 s n X ẵReZi, ị  ReZi, ị2 RMSD%ị ẳ  100 ẵReZi, ị2 iẳ1 (b) CC = 0.013 200 150 100 3.05 Test#1466 at 28.4°C Test#1522 at 21.7°C Max CC = 0.983 250 ð4Þ where Zi,0 is the impedance function at !i for the baseline case, Zi,1 is a concurrent impedance at !i, and n is the number of frequency points A large value of RMSD means stronger indication of damage occurrence in the concurrent case with respect to the baseline case Real (Z (w)) Real (Z (w)) 300 which indicates the effectiveness of the present CC-based damage detection method using the EFS For the purpose of comparison, three other damage measures for the impedance-based SHM were additionally considered: (a) maximum CCs without EFSs, (b) RMSD with EFSs, and (c) RMSD without EFSs Results of the additional analyses are displayed in Figures 14(b), 15(a) and 15(b) The RMSD values were evaluated as: 3.1 3.15 Frequency (Hz) × 104 −2 3.05 3.1 3.15 Frequency (Hz) × 104 Figure 10 Impedance data for two damage cases with an mm cut (a) Original impedance signatures, (b) Normalized impedance signatures after EFS (Reference: Test #1466) (a) 160 Test#1 at 22.6°C Test#951 at 22.6°C 150 CC = −0.055 130 120 110 100 Test#1 at 22.6°C Test#951 at 22.6°C Real (Z (w)) 140 Real (Z (w)) (b) Max CC = 0.920 90 80 3.05 3.1 3.15 Frequency (Hz) 104 × −2 3.05 3.1 3.15 Frequency (Hz) × 104 Figure 11 Impedance data for an intact case and a damage case with a mm cut (a) Original impedance signatures, (b) Normalized impedance signatures after EFS (Reference: Test #1) 374 K.-Y KOO ET AL Test#1 at 22.6°C Test#1116 at 22.6°C (a) 180 (b) Test#1 at 22.6°C Test#1116 at 22.6°C CC = −0.045 Max CC = 0.851 Real (Z (w)) Real (Z (w)) 160 140 120 100 80 3.05 3.1 −2 3.05 3.15 × 104 Frequency (Hz) 3.1 3.15 × 104 Frequency (Hz) Figure 12 Impedance data for an intact case and a damage case with a mm cut (a) Original impedance signatures, (b) Normalized impedance signatures after EFS (Reference: Test #1) Test#1 at 22.6°C (b) Test#1 at 22.6°C (a) 180 Test#1340 at 22.6°C Test#1340 at 22.6°C CC = −0.101 Max CC = 0.680 Real (Z (w)) Real (Z (w)) 160 140 120 100 80 3.05 3.1 3.15 Frequency (Hz) 104 × −2 3.05 3.1 3.15 × 104 Frequency (Hz) Figure 13 Impedance data for an intact case and a damage case with an mm cut (a) Original impedance signatures, (b) Normalized impedance signatures after EFS (Reference: Test #1) Intact (a) (b) 1.2 0.9 0.85 2mm cut 0.8 4mm cut 0.75 0.65 8mm cut 200 400 600 800 Test no 1000 35 30 25 20 15 10 1200 1400 1600 Temperature (°C) 0.7 0.8 0.6 0.4 0.2 −0.2 35 30 25 20 15 10 200 400 600 800 1000 1200 1400 Temperature (°C) Max CC w/o EFS 0.95 Max CC w/ EFS mm cut mm cut mm cut 1600 Test no Figure 14 Cross-correlation coefficients for all cases with respect to Test #1 (a) After the effective frequency shift, (b) Before the effective frequency shift 375 Impedance-based SHM Technique RMSD w/o EFS (%) 100 mm cut mm cut 35 30 25 20 15 10 200 400 600 800 1000 1200 1400 1600 Temperature (°C) 50 mm cut mm cut mm cut 250 mm cut RMSD w/ EFS (%) Intact (b) 150 200 150 100 50 35 30 25 20 15 10 0 200 400 Test no 600 800 1000 1200 1400 Temperature (°C) (a) 1600 Test no Figure 15 Root mean square deviations for all cases from Test #1 (a) After the effective frequency shift, (b) Before the effective frequency shift Figures 14(b) and 15(b) present the maximum CCs and RMSD results without applying the EFSs, while Figures 14(a) and 15(a) show the results incorporating the EFSs In the case of the RMSD-based method, the EFSs correspond to the minimum RMSDs after the shift It is noted that both CC-based and RMSD-based methods without the EFSs did not provide good damage diagnostic results, while both results with the EFSs showed successful damage detections even under temperature varying environment The general performance of the CC-based and RMSD-based methods with the respective EFSs is found to be equally good DAMAGE DETECTION USING OUTLIER ANALYSIS DV Dth Outlier, xV Out-Dimensional decision boundary if DV >Dth then xV is outlier! xV came from a damaged state Figure 16 Multivariate outlier analysis (novelty detection) Outlier Analysis An automated damage diagnostic system without requiring any a priori mathematical model of the structure, may provide an efficient SHM tool for real structures In order to satisfy this requirement, a so-called ‘novelty detection’ outlier analysis method has emerged as a robust unsupervised learning pattern recognition tool for damage detection of structures (Worden et al., 2000; Park et al., 2003b) The outlier analysis aims to establish simply whether or not a new pattern is significantly different from the previous patterns, at the same time automatically ignoring any negligible differences, such as random fluctuations due to noise That is, an outlier is an observation that is significantly different from the rest of the population and therefore the outlier is believed to be generated by an alternate mechanism (Barnett and Lewis, 1994) Assuming a multivariate normal distribution (MVN) of sample patterns, the deviation of the candidate outlier (x), from the rest of the population can be measured by Mahalanobis square distance (MSD) measure, given by: D& ẳ x&  ịT 1 x&  Þ ð5Þ where  is the mean of the samples,  is the covariance matrix of the samples, and D is a deviation measure For the SHM applications,  and  are exclusively computed from the measurement for the baseline system without including potential outliers The deviation measure D is then compared with the threshold value Dth Herein, if D4Dth, x is an outlier, which means x came from a damaged state The basic concept of this outlier analysis is illustrated in Figure 16 The threshold value depends on both the number and the dimension of the data set To investigate the effect of the dimension and the size of the data set, a Monte Carlo method is used to 376 0.8 0.4 0.6 Take Di = max di i 0.4 0.2 0.2 −2 Repeat n times to get the distribution of Di −2 10 Dth 1.05 0.95 0.9 0.85 0.8 0.75 0.7 0.65 thr1 = 0.942 thr2 = 0.884 mm cut thr3 = 0.807 mm cut mm cut Figure 17 Establishment of threshold through outlier analysis arrive at a threshold value First, Xn which is a m  p (dimension of data  number of observations) matrix of the measurements is constructed as: 200 400 600 800 1000 1200 1400 35 30 25 20 15 10 1600 Temperature (°C) 99.5% 0.8 confidence level 0.6 Max CC w/ EFS K.-Y KOO ET AL Test no Figure 18 Damage detection through the outlier analysis Xn ẳ ẵx1 , x2 , x3 , , xp  xi : input measurement vector: ð6Þ For this study, the input vector xi is taken as the CC values calculated using Equation (2) Then, the MSD is computed for all xi as di ðxi , ị ẳ xi  ịT 1 xi  ị i ¼ p ð7Þ where  and  are estimated from Xn The maximum MSD among di is selected and stored as Di ¼ max di : i ð8Þ The previous steps are repeated n times to have a large population of Di, and the probability distribution function (PDF) of Di is empirically estimated The threshold value Dth can be established from the estimated PDF for a prescribed confidence level, as shown in Figure 17 Damage Detection using Outlier Analysis The optimal threshold values for more systematic damage detection considering the fluctuations in the CC values were investigated with a statistical confidence level (C.L.) of 99.5% through an outlier analysis In this analysis, all the CC values before each damage step were utilized as the basis data to update the threshold level of the outlier analysis The results are shown in Figure 18 As expected, it has been found that after each cut damage of 2, 4, and mm is inflicted, the CC values dropped abruptly under the corresponding threshold values (thr1: 0.942, thr2: 0.884, and thr3: 0.807), so that reliable SHM and systematic damage detection may be achieved under a temperature varying environment by the present impedance-based method However, it is noted that the threshold levels shall be properly selected considering the daily temperature range and the minimum target damage level for detection, and continuous updating of the threshold level is needed as more data are available CONCLUSIONS The feasibility of the impedance-based structural health monitoring (SHM) technique to diagnose the integrity of the structures has been investigated under the temperature varying environment The temperature variation resulted in a significant variation in the impedance measurement, particularly a frequency shift in the impedance, which may lead to erroneous diagnostic results regarding the integrity of real structures including civil, mechanical, and aerospace structures In order to minimize the effects of the temperature variations, a previously proposed temperature compensation technique based on cross-correlation between the reference-impedance data and a concurrent impedance data is revisited In this study, the cross-correlation coefficient (CC) with an effective frequency shift (EFS), which is defined as the frequency shift causing two impedance data to have the maximum correlation, was utilized To promote a practical use of the proposed SHM strategy, an automated continuous monitoring framework using MATLABÕ has been developed and incorporated with the current hardware system The proposed techniques were applied to health monitoring of a lab-sized steel truss bridge member with the maximum temperature variation of 218C From the experimental study, it has been found that the EFS method may significantly reduce the temperature variation effects on the damage detection However, the CC values have still shown significant fluctuations even after applying the EFS method Therefore, an outlier analysis has been also employed to determine proper threshold levels for more systematic damage detection considering the fluctuations in the CCs The results of the present experimental study demonstrated that the proposed impedance-based automated SHM technique incorporating the EFS and the outlier analysis can be effectively used for diagnosing the structural integrity, even with the presence of temperature variations Impedance-based SHM Technique ACKNOWLEDGMENTS This work was jointly supported by the Smart InfraStructure Technology Center (SISTeC) at KAIST sponsored by the Korea Science and Engineering Foundation, and a grant (code PM43200) from ‘‘Development of utilization technique for tide and tidal current energy’’ funded by Ministry of Maritime Affairs and Fisheries of Korean government This financial support is greatly appreciated REFERENCES Barnett, V and Lewis, T 1994 Outliers in Statistical Data, John Wiley and Sons, England Bhalla, S., Naidu, A.S.K., Ong, C.W and Soh, C.K 2002 ‘‘Practical Issues in the Implementation of Electro-mechanical Impedance Technique for NDE,’’ In: Proceedings of SPIE Conference on Smart Structures, Devices, and Systems, December 16–18, Melbourne, Australia, 4935:484–494 Bhalla, S., Naidu, A.S.K and Soh, C.K 2003 ‘‘Influence of Structure-Actuator Interactions and Temperature on Piezoelectric Mechatronic Signatures for NDE,’’ In: Proceedings of SPIE Conference on Smart Materials, Structures, and Systems, 5062:263–269 Giurgiutiu, V and Rogers, C.A 1997 ‘‘Electro-mechanical (E/M) Impedance Method for Structural Health Monitoring and Nondestructive Evaluation,’’ In: Proceedings of International Workshop on Structural Health Monitoring, September 18–20, Stanford, CA, pp 433–444 Giurgiutiu, V., Reynolds, A and Rogers, C.A 1999 ‘‘Experimental Investigation of E/M Impedance Health Monitoring of Spotwelded Structure Joints,’’ Journal of Intelligent Material Systems and Structures, 10:802–812 Grisso, B.L and Inman, D.J 2005 ‘‘Developing an Autonomous OnOrbit Impedance-based SHM System for Thermal Protection Systems,’’ In: Proceedings of the 5th International Workshop on Structural Health Monitoring, September 12–14, Stanford, CA, pp 435–442 Liang, L., Sun, F.P and Rogers, C.A 1994 ‘‘Coupled Electromechanical Analysis of Adaptive Material SystemsDetermination of the Actuator Power Consumption and System Energy Transfer,’’ Journal of Intelligent Material Systems and Structures, 5:12–20 Mascarenas, D.L., Todd, M.D., Park, G and Farrar, C.R 2006 ‘‘A Miniaturized Electromechanical Impedance-based Node for the Wireless Interrogation of Structural Health,’’ In: Proceeding 377 of SPIE’s 13th Annual International Symposium on Smart Structures and Materials, 6177, March 28 Park, G., Kabeya, K., Cudney, H and Inman, D.J 1999a ‘‘Impedance-based Structural Health Monitoring for Temperature Varying Applications,’’ JSME International Journal, 42(2):249–258 Park, G., Cudney, H.H and Inman, D.J 1999b ‘‘Impedance-based Health Monitoring Technique of Massive Structures and Hightemperature Structures,’’ In: Proceedings of SPIE Conference on Smart Structures and Materials, Newport Beach, CA, 3670, pp 461–469 Park, G., Cudney, H.H and Inman, D.J 2000 ‘‘Impedance-based Health Monitoring of Civil Structural Components,’’ ASCE Journal of Infrastructure Systems, 6:153–160 Park, G., Sohn, H., Farrar, C.R and Inman, D.J 2003a ‘‘Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward,’’ The Shock and Vibration Digest, 35(6):451–463 Park, G., Rutherford, A.C., Sohn, H and Farrar, C.R 2003b ‘‘An Outlier Analysis Framework for Impedance-based Structural Health Monitoring,’’ Journal of Sound and Vibration, 35(6): 451–463 Park, S., Yun, C.B., Roh, Y and Lee, J.J 2005 ‘‘Health Monitoring of Steel Structures using Impedance of Thickness Modes at PZT Patches,’’ Smart Structures and Systems, 1(4): 339–353 Park, S., Ahmad, S., Yun, C.-B and Roh, Y 2006a ‘‘Multiple Crack Detection of Concrete Structures Using Impedance-based Structural Health Monitoring Techniques,’’ Experimental Mechanics, 46:609–618 Park, S., Yun, C.-B and Inman, D.J 2006b ‘‘Wireless Structural Health Monitoring using an Active Sensing Node,’’ International Journal of Steel Structures, 6(5):361–368 Soh, C.K., Tseng, K., Bhalla, S and Gupta, A 2000 ‘‘Performance of Smart Piezoceramic Patches in Health Monitoring of a RC Bridge,’’ Smart Materials and Structures, 9:533–542 Sun, F., Chaudhry, Z and Rogers, C.A 1995 ‘‘Automated Real-time Structure Health Monitoring via Signature Pattern Recognition,’’ In: Proceedings of SPIE Conference on Smart Structures and Materials, 2443:236–247 Tseng, K.K., Soh, C.K., Gupta, A and Bhalla, S 2000 ‘‘Health Monitoring of Civil Infrastructures Using Smart Piezoceramic Transducers,’’ In: 2nd International Conference on Computational Methodology for Smart Structures and Materials, pp 153–162 Worden, K., Manson, G and Fieller, N.J 2000 ‘‘Damage Detection Using Outlier Analysis,’’ Journal of Sound and Vibration, 229: 647–667 Zagrai, A.N and Giurgiutiu, V 2001 ‘‘Electro-Mechanical Impedance Method for Crack Detection in Thin Wall Structures,’’ 3rd Int Workshop of Structural Health Monitoring, Stanford Univ., CA, September 12–14  ... measurement (Sun et al. , 1995; Park et al. , 1999a,b; Bhalla et al. , 2003) Sun et al (1995) used a temperature compensation method based on crosscorrelation to correct the horizontal shift in the... Soh et al. , 2000; Tseng et al. , 2000; Zagrai and Giurgiutiu, 2001; Bhalla et al. , 2002) However, there are still many impediments to the practical application of the technique for SHM of real...368 K.-Y KOO ET AL telemetry requirements resulted in an on-board active sensor system (Grisso and Inman, 2005; Mascarenas et al. , 2006; Park et al. , 2006b) The on-board active