Bayesian Combination of Weighted Principal-Component Analysis for Diagnosing Sensor Faults in Structural Monitoring Systems
Publication: Journal of Engineering Mechanics
Volume 143, Issue 9
Abstract
It is essential to diagnose, i.e., detect and isolate, potential sensor faults for structural health monitoring to guarantee reliable condition evaluations. This paper proposes an innovative method called weighted principal-component analysis for sensor-fault detection and isolation. It is first illustrated that the fault sensitivity of each principal direction of traditional principal-component analysis is different from others for the same fault occurring in a certain sensor. Then, a fault-sensitive factor is theoretically derived to quantify the fault sensitivities. Based on that, a weighted fault-detection statistic determined according to the difference in fault sensitivities is developed and shown to have enhanced fault-detection ability. Bayesian inference is used to integrate all the weighted statistics corresponding to all the sensors to quickly judge whether a sensor fault occurred. Meanwhile, contribution analysis is used to establish a fault isolation index to identify the specific faulty sensor. Case studies using numerical simulation and a benchmark model demonstrate that the new proposed method is excellent and superior to the traditional approach.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 51625802, 51478081), the 973 Program (Grant No. 2015CB060000), and the Science Fund for Distinguished Young Scholars of Dalian (Grant No. 2015J12JH209).
References
Abazarsa, F., Nateghi, F., Ghahari, S. F., and Tacir, E. (2015). “Extended blind modal identification technique for nonstationary excitations and its verification and validation.” J. Eng. Mech., 04015078.
Abdelghani, M., and Friswell, M. I. (2004). “Sensor validation for structural systems with additive sensor faults.” Struct. Health Monit., 3(3), 265–275.
Abdelghani, M., and Friswell, M. I. (2007). “Sensor validation for structural systems with multiplicative sensor faults.” Mech. Syst. Signal Process., 21(1), 270–279.
Alcala, C. F., and Qin, S. J. (2011). “Analysis and generalization of fault diagnosis methods for process monitoring.” J. Process Control, 21(3), 322–330.
Balaban, E., Saxena, A., Bansal, P., Goebel, K. F., and Curran, S. (2009). “Modeling, detection, and disambiguation of sensor faults for aerospace applications.” IEEE Sens. J., 9(12), 1907–1917.
Caicedo, J. M., Dyke, S. J., and Johnson, E. A. (2004). “Natural excitation technique and eigensystem realization algorithm for phase I of the IASC-ASCE benchmark problem: Simulated data.” J. Eng. Mech., 49–60.
Catbas, N., Gokce, H. B., and Frangopol, D. M. (2013). “Predictive analysis by incorporating uncertainty through a family of models calibrated with structural health-monitoring data.” J. Eng. Mech., 712–723.
Chen, F., and Qiao, P. (2015). “Probabilistic damage modeling and service-life prediction of concrete under freeze-thaw action.” Mater. Struct., 48(8), 2697–2711.
Dunia, R., Qin, S. J., Edgar, T. F., and McAvoy, T. J. (1996). “Identification of faulty sensors using principal component analysis.” AIChE J., 42(10), 2797–2812.
Fan, W., and Qiao, P. (2011). “Vibration-based damage identification methods: A review and comparative study.” Struct. Health Monit., 10(1), 83–111.
Ge, Z., Zhang, M., and Song, Z. (2010). “Nonlinear process monitoring based on linear subspace and Bayesian inference.” J. Process Control, 20(5), 676–688.
Hernandez-Garcia, M. R., and Masri, S. F. (2014). “Application of statistical monitoring using latent-variable techniques for detection of faults in sensor networks.” J. Intell. Mater. Syst. Struct., 25(2), 121–136.
Huang, H. B., Yi, T. H., and Li, H. N. (2016). “Canonical correlation analysis based fault diagnosis method for structural monitoring sensor networks.” Smart Struct. Syst., 17(6), 1031–1053.
Huang, H. B., Yi, T. H., and Li, H. N. (2017). “Sensor fault diagnosis for structural health monitoring based on statistical hypothesis test and missing variable approach.” J. Aerosp. Eng., 1–14.
Huo, L., Song, G., Nagarajaiah, S., and Li, H. (2012). “Semi-active vibration suppression of a space truss structure using a fault tolerant controller.” J. Vib. Control, 18(10), 1436–1453.
Jiang, Q., and Yan, X. (2014). “Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference.” Chem. Intell. Lab. Syst., 136, 121–137.
Jiang, Q., Yan, X., and Zhao, W. (2013). “Fault detection and diagnosis in chemical processes using sensitive principal component analysis.” Ind. Eng. Chem. Res., 52(4), 1635–1644.
Johnson, E. A., Lam, H. F., Katafygiotis, L. S., and Beck, J. L. (2004). “Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data.” J. Eng. Mech., 3–15.
Kallinikidou, E., Yun, H. B., Masri, S. F., Caffrey, J. P., and Sheng, L. H. (2013). “Application of orthogonal decomposition approaches to long-term monitoring of infrastructure systems.” J. Eng. Mech., 678–690.
Kerschen, G., De Boe, P., Golinval, J. C., and Worden, K. (2005). “Sensor validation using principal component analysis.” Smart Mater. Struct., 14(1), 36–42.
Kong, X., Cai, C. S., and Kong, B. (2015). “Damage detection based on transmissibility of a vehicle and bridge coupled system.” J. Eng. Mech., 04014102.
Kresta, J. V., MacGregor, J. F., and Marlin, T. E. (1991). “Multivariate statistical monitoring of process operating performance.” Can. J. Chem. Eng., 69(1), 35–47.
Kullaa, J. (2010). “Sensor validation using minimum mean square error estimation.” Mech. Syst. Signal Process., 24(5), 1444–1457.
Kullaa, J. (2013). “Detection, identification, and quantification of sensor fault in a sensor network.” Mech. Syst. Signal Process., 40(1), 208–221.
Li, H. N., Yi, T. H., Ren, L., Li, D. S., and Huo, L. S. (2014). “Reviews on innovations and applications in structural health monitoring for infrastructures.” Struct. Monit. Maint., 1(1), 1–45.
Li, Z., Koh, B. H., and Nagarajaiah, S. (2007). “Detecting sensor failure via decoupled error function and inverse input-output model.” J. Eng. Mech., 1222–1228.
Lo, C., Lynch, J. P., and Liu, M. (2016). “Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks.” Mech. Syst. Signal Process., 66–67, 470–484.
Lu, Y., and Michaels, J. E. (2009). “Feature extraction and sensor fusion for ultrasonic structural health monitoring under changing environmental conditions.” IEEE Sensors J., 9(11), 1462–1471.
MATLAB [Computer software]. MathWorks, Natick, MA.
Qiao, P., and Chen, F. (2013). “Cohesive fracture and probabilistic damage analysis of freezing-thawing degradation of concrete.” Constr. Build. Mater., 47(10), 879–887.
Qin, S. J. (2003). “Statistical process monitoring: Basics and beyond.” J. Chemom., 17(8–9), 480–502.
Rao, A. R. M., Kasireddy, V., Gopalakrishnan, N., and Lakshmi, K. (2014). “Sensor fault detection in structural health monitoring using null subspace-based approach.” J. Intell. Mater. Syst. Struct., 26(2), 172–185.
Sbarufatti, C., Manes, A., and Giglio, M. (2014). “Application of sensor technologies for local and distributed structural health monitoring.” Struct. Control Health Monit., 21(7), 1057–1083.
Sharifi, R., Kim, Y., and Langari, R. (2010). “Sensor fault isolation and detection of smart structures.” Smart Mater. Struct., 19(10), 105001.
Smarsly, K., and Law, K. H. (2014). “Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy.” Adv. Eng. Software, 73, 1–10.
Tamura, M., and Tsujita, S. (2007). “A study on the number of principal components and sensitivity of fault detection using PCA.” Comput. Chem. Eng., 31(9), 1035–1046.
Valle, S., Li, W., and Qin, S. J. (1999). “Selection of the number of principal components: The variance of the reconstruction error criterion with a comparison to other methods.” Ind. Eng. Chem. Res., 38(11), 4389–4401.
Wang, H., and Song, G. (2011). “Fault detection and fault tolerant control of a smart base isolation system with magneto-rheological damper.” Smart Mater. Struct., 20(8), 298–300.
Wang, S., and Xiao, F. (2004). “AHU sensor fault diagnosis using principal component analysis method.” Energy Build., 36(2), 147–160.
Wang, T., Song, G., Wang, Z., and Li, Y. (2013). “Proof-of-concept study of monitoring bolt connection status using a piezoelectric based active sensing method.” Smart Mater. Struct., 22(8), 1–5.
Yang, Y., and Nagarajaiah, S. (2012). “Time-frequency blind source separation using independent component analysis for output-only modal identification of highly damped structures.” J. Struct. Eng., 1780–1793.
Yang, Y., and Nagarajaiah, S. (2013). “Blind modal identification of output-only structures in time-domain based on complexity pursuit.” Earthquake Eng. Struct. D., 42(13), 1885–1905.
Yi, T. H., Li, H. N., and Gu, M. (2011). “Optimal sensor placement for structural health monitoring based on multiple optimization strategies.” Struct. Des. Tall Special Build., 20(7), 881–900.
Yi, T. H., Li, H. N., and Zhang, X. D. (2015). “Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm.” Struct. Control Health Monit., 22(1), 123–138.
Yuen, K. V., Au, S. K., and Beck, J. L. (2004). “Two-stage structural health monitoring approach for Phase I benchmark studies.” J. Eng. Mech., 16–33.
Yuen, K. V., and Kuok, S. C. (2015). “Efficient Bayesian sensor placement algorithm for structural identification: A general approach for multi-type sensory systems.” Earthquake Eng. Struct. D., 44(5), 757–774.
Zhang, W., Cai, C. S., and Pan, F. (2013). “Nonlinear fatigue damage assessment of existing bridges considering progressively deteriorated road conditions.” Eng. Struct., 56(6), 1922–1932.
Zhang, W., Cai, C. S., Pan, F., and Zhang, Y. (2014). “Fatigue life estimation of existing bridges under vehicle and non-stationary hurricane wind.” J. Wind Eng. Ind. Aerodyn., 133, 135–145.
Zhao, C., and Gao, F. (2014). “Fault-relevant principal component analysis (FPCA) method for multivariate statistical modeling and process monitoring.” Chemom. Intell. Lab. Syst., 133, 1–16.
Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 143 • Issue 9 • September 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Nov 17, 2015
Accepted: Mar 16, 2017
Published online: Jun 14, 2017
Published in print: Sep 1, 2017
Discussion open until: Nov 14, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.