e-ISSN 2231-8526
ISSN 0128-7680
Masood Ahmad and Rosmiwati Mohd-Mokhtar
Pertanika Journal of Science & Technology, Volume 30, Issue 1, January 2022
DOI: https://doi.org/10.47836/pjst.30.1.04
Keywords: Fault detection, Kalman filter, LTI system, model-based techniques, residual generation
Published on: 10 January 2022
With the ongoing increase in complexity, less tolerance to performance degradation and safety requirements of practical systems has increased the necessity of fault detection (FD) as early as possible. During the last few decades, many research findings have been developed in fault diagnosis that addresses the issue of fault detection and isolation in linear and nonlinear systems. The paper’s objective is to present a survey on various state-of-art model-based FD techniques developed for linear time-invariant (LTI) systems for the interested readers to learn about recent development in this field. Model-based FD techniques for LTI systems are classified as parameter-estimation methods, parity-space-based methods, and observer-based methods. The background and recent progress, in context to fault detection, of each of these methods and their practical applications are discussed in this paper. Furthermore, two different FD techniques are compared via analytical equations and simulation results obtained from the DC motor model. In the end, possible future research directions in model-based FD, particularly for the LTI system, are highlighted for prosperous researchers. A comparison and emerging research topic make this contribution different from the existing survey papers on FD.
Aguilera, F., de la Barrera, P. M., De Angelo, C. H., & Espinoza Trejo, D. R. (2016). Current-sensor fault detection and isolation for induction-motor drives using a geometric approach. Control Engineering Practice, 53, 35-46. https://doi.org/10.1016/j.conengprac.2016.04.014
Ahmad, M., & Mohd-Mokhtar, R. (2020). H-indexed fault sensitive filter design for linear discrete-time uncertain DC motor system. Interciencia, 45(10), 60-74.
Ahmad, S., Ali, N., Ayaz, M., & Ahmad, E. (2017). Design of robust fault detection filter using algorithm for a class of LTI systems. In 13th International Conference on Emerging Technologies (ICET) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/ICET.2017.8281720
Bachir, S., Tnani, S., Trigeassou, J. C., & Champenois, G. (2006). Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Transactions on Industrial Electronics, 53(3), 963-973. https://doi.org/10.1109/TIE.2006.874258
Belmokhtar, K., Ibrahim, H., & Merabet, A. (2015). Online parameter identification for a DFIG driven wind turbine generator based on recursive least squares algorithm. In IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 965-969). IEEE Publishing. https://doi.org/10.1109/CCECE.2015.7129406
Blanke, M., Kinnaert, M., Lunze, J., & Staroswiecki, M. (2015). Diagnosis and fault-tolerant control (3rd Ed.). Springer.
Bøgh, S. (1995). Multiple hypothesis-testing approaches to FDI for the industrial actuator benchmark. Control Engineering Practice, 3(12), 1763-1768. https://doi.org/10.1016/0967-0661(95)00191-V
Chen, W., Ding, S. X., Haghani, A., Naik, A., Khan, A. Q., & Yin, S. (2011). Observer-based FDI schemes for wind turbine benchmark. IFAC Proceedings Volumes, 44(1), 7073-7078. https://doi.org/10.3182/20110828-6-IT-1002.03469
Cho, S., Gao, Z., & Moan, T. (2018). Model-based fault detection, fault isolation, and fault-tolerant control of a blade pitch system in floating wind turbines. Renewable Energy, 120, 306-321. https://doi.org/10.1016/j.renene.2017.12.102
Da, R., & Lin, C. F. (1996). Sensitivity analysis of the state chi-square test. IFAC Proceedings Volumes, 29(1), 6596-6601. https://doi.org/10.1016/S1474-6670(17)58741-8
Dai, X., Gao, Z., Breikin, T., & Wang, H. (2009). Disturbance attenuation in fault detection of gas turbine engines: A discrete robust observer design. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 39(2), 234-239. https://doi.org/10.1109/TSMCC.2008.2005845
Denkena, B., Bergmann, B., & Stoppel, D. (2020). Reconstruction of process forces in a five-axis milling center with a LSTM neural network in comparison to a model-based approach. Journal of Manufacturing and Materials Processing, 4(3), Article 62. https://doi.org/10.3390/jmmp4030062
Ding, S. X. (2013). Model-based fault diagnosis techniques: design schemes, algorithms, and tools. Springer Science & Business Media.
Ding, S. X. (2014). Data-driven design of fault diagnosis and fault-tolerant control systems. Springer. https://doi.org/10.1007/978-1-4471-6410-4
Ding, S. X., & Frank, P. M. (1990). Fault detection via factorization approach. Systems & Control Letters, 14(5), 431-436. https://doi.org/10.1016/0167-6911(90)90094-B
Ding, S. X., Jeinsch, T., Frank, P. M., & Ding, E. L. (2000). A unified approach to the optimization of fault detection systems. International Journal of Adaptive Control and Signal Processing, 14(7), 725-745. https://doi.org/10.1002/1099-1115(200011)
Ding, S. X., Zhang, P., & Frank, P. M. (2003). Threshold calculation using LMI-technique and its integration in the design of fault detection systems. In 42nd IEEE International Conference on Decision and Control (pp. 469-474). IEEE Publishing. https://doi.org/10.1109/CDC.2003.1272607
Do, M. H., Koenig, D., & Theilliol, D. (2018). Robust H∞ proportional-integral observer for fault diagnosis: Application to vehicle suspension. IFAC-PapersOnLine, 51(24), 536-543. https://doi.org/10.1016/j.ifacol.2018.09.628
Doraiswami, R., & Cheded, L. (2013). A unified approach to detection and isolation of parametric faults using a Kalman filter residual-based approach. Journal of the Franklin Institute, 350(5), 938-965. https://doi.org/10.1016/j.jfranklin.2013.01.005
Dybkowski, M., & Klimkowski, K. (2017). Speed sensor fault detection algorithm for vector control methods based on the parity relations. In 2017 19th European Conference on Power Electronics and Applications (pp. 1-5). IEEE Publishing. https://doi.org/10.23919/EPE17ECCEEurope.2017.8099342
Farhat, A., & Koenig, D. (2015). PI robust fault detection observer for a class of uncertain switched systems using LMIs. IFAC-PapersOnLine, 48(21), 125-130. https://doi.org/10.1016/j.ifacol.2015.09.515
Frank, P. M., Ding, S. X., & Koppen-Seliger B. (2000). Current developments in the theory of FDI. IFAC Proceeding Volumes: 4th IFAC Symposium on Fault Detection, Supervision, and Safety for Technical Processes, 33(11), 17-28.
Franklin, G. F., David-Powell, J., & Emami-Naeini, A. (2019). Feedback control of dynamic systems (8th Ed.). Pearson Prentice Hall.
Gannouni, F., & Hmida, F. B. (2017). Simultaneous state and fault estimation for linear stochastic systems. In 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 59-66). IEEE Publishing. https://doi.org/10.1109/STA.2017.8314965
Gao, Z., Cecati, C., & Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques-part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757-3767. https://doi.org/10.1109/TIE.2015.2417501
Gautam, S., Tamboli, P. K., Patankar, V. H., Duttagupta, S. P., & Roy, K. (2017). Performance evaluation of statistical method for incipient fault detection under noisy environment. IFAC-PapersOnLine, 50(1), 15728-15733. https://doi.org/10.1016/j.ifacol.2017.08.2415
Gertler, J. J. (2017). Fault detection and diagnosis in engineering systems. CRC Press. https://doi.org/10.1201/9780203756126
Hajiyev, C., & Soken, H. E. (2013). Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults. Aerospace Science and Technology, 28(1), 376-383. https://doi.org/10.1016/j.ast.2012.12.003
Herrera, L., & Yao, X. (2018). Parameter identification approach to series DC arc fault detection and localization. In IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 497-501). IEEE Publishing. https://doi.org/10.1109/ECCE.2018.8557679
Hur, H., & Ahn, H. S. (2014). Unknown input H-infinity observer-based localization of a mobile robot with sensor failure. IEEE/ASME Transactions on Mechatronics, 19(6), 1830-1838. https://doi.org/10.1109/TMECH.2014.2298034
Hwang, W., & Huh, K. (2015). Fault detection and estimation for electromechanical brake systems using parity space approach. Journal of Dynamic Systems, Measurement, and Control, 137(1), Article 014504. https://doi.org/10.1115/1.4028184
Isermann, R. (1984). Process fault detection based on modeling and estimation methods - A survey. Automatica, 20(4), 387-404. https://doi.org/10.1016/0005-1098(84)90098-0
Isermann, R. (1997). Supervision, fault-detection, and fault-diagnosis methods - An introduction. Control Engineering Practice, 5(5), 639-652. https://doi.org/10.1016/S0967-0661(97)00046-4
Isermann, R. (2006). Fault-diagnosis systems. Springer.
Jesica, E., & Poznyak, A. (2018). Parameter estimation in continuous-time stochastic systems with correlated noises using the Kalman filter and least squares method. IFAC-PapersOnLine, 51(13), 309-313. https://doi.org/10.1016/j.ifacol.2018.07.296
Jie, C., & Patton, R. J. (2012). Robust model-based fault diagnosis for dynamic systems. Springer. https://doi.org/10.1007/978-1-4615-5149-2
Jokic, I., Zecevic, Z., & Krstajic, B. (2018). State-of-charge estimation of lithium-ion batteries using extended Kalman filter and unscented Kalman filter. In 23rd International Scientific-Professional Conference on Information Technology (IT) (pp. 1-4). IEEE Publishing. https://doi.org/10.1109/SPIT.2018.8350462
Khang, H. V., Kandukuri, S., Pawlus, W., & Robbersmyr, K. G. (2018). Parameter identification of a winding function-based model for fault detection of induction machines. In Eighth International Conference on Information Science and Technology (ICIST) (pp. 200-205). https://doi.org/10.1109/ICIST.2018.8426188
Khazraj, H., Faria da Silva, F., & Bak, C. L. (2016). A performance comparison between extended Kalman filter and unscented Kalman filter in power system dynamic state estimation. In 51st International Universities Power Engineering Conference (UPEC) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/UPEC.2016.8114125
Kleilat, I., Al-Sheikh, H., Moubayed, N., & Hoblos, G. (2018). Robust fault diagnosis of sensor faults in power converter used in hybrid electric vehicle. IFAC-PapersOnLine, 51(24), 326-331. https://doi.org/10.1016/j.ifacol.2018.09.597
Li, L., Ding, S. X., Zhang, Y., & Yang, Y. (2016). Optimal fault detection design via iterative estimation methods for industrial control systems. Journal of the Franklin Institute, 353(2), 359-377. https://doi.org/10.1016/j.jfranklin.2015.12.002
Li, W., Zhu, Z., Zhou, G., & Chen, G. (2013). Optimal H i /H ∞ fault-detection filter design for uncertain linear time-invariant systems: An iterative linear matrix inequality approach. IET Control Theory & Applications, 7(8), 1160-1167. https://doi.org/10.1049/iet-cta.2012.0954
Lijia, C., Yu, T., & Guo, Z. (2019). Adaptive observer-based fault detection and active tolerant control for unmanned aerial vehicles attitude system. IFAC-PapersOnLine, 52(24), 47-52. https://doi.org/10.1016/j.ifacol.2019.12.379
Liu, X., Wang, Z., Wang, Y., & Shen, Y. (2018). Dynamic threshold computation in fault detection for discrete-time linear systems. In 2018 Chinese Control And Decision Conference (CCDC) (pp. 2241-2246). IEEE Publishing. https://doi.org/10.1109/CCDC.2018.8407499
Liu, Z., & He, H. (2017). Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter. Applied Energy, 185, 2033-2044. https://doi.org/10.1016/j.apenergy.2015.10.168
Marzat, J., Piet-Lahanier, H., Damongeot, F., & Walter, E. (2012). Model-based fault diagnosis for aerospace systems: A survey. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 226(10), 1329-1360. https://doi.org/10.1177/0954410011421717
Mehra, R. K., & Peschon, J. (1971). An innovations approach to fault detection and diagnosis in dynamic systems. Automatica, 7(5), 637-640. https://doi.org/10.1016/0005-1098(71)90028-8
Na, Y., & Ahmad, M. (2019). A fault detection scheme for switched systems with noise under asynchronous switching. In 9th International Conference on Information Science and Technology (ICIST) (pp. 258-262). IEEE Publishing. https://doi.org/10.1109/ICIST.2019.8836838
Nadarajan, S., Panda, S. K., Bhangu, B., & Gupta, A. K. (2016). Online model-based condition monitoring for brushless wound-field synchronous generator to detect and diagnose stator windings turn-to-turn shorts using extended Kalman filter. IEEE Transactions on Industrial Electronics, 63(5), 3228-3241. https://doi.org/10.1109/TIE.2016.2535959
Nikiforov, I., Varavva, V., & Kireichikov, V. (1993). Application of statistical fault detection algorithms to navigation systems monitoring. Automatica, 29(5), 1275-1290. https://doi.org/10.1016/0005-1098(93)90050-4
Odendaal, H. M., & Jones, T. (2014). Actuator fault detection and isolation: An optimized parity space approach. Control Engineering Practice, 26, 222-232. https://doi.org/10.1016/j.conengprac.2014.01.013
Patton, R. J., & Chen, J. (2000). On eigenstructure assignment for robust fault diagnosis. International Journal of Robust and Nonlinear Control, 10(14), 1193-1208. https://doi.org/10.1002/1099-1239(20001215)10:14<1193::AID-RNC523>3.0.CO;2-R
Perrin, O., Basseville, M., Sorine, M., & Zhang, Q. (2004). On-board diesel particulate filter fault detection using an adaptive observer. IFAC Proceedings Volumes, 37(22), 367-372. https://doi.org/10.1016/S1474-6670(17)30371-3
Pourasghar, M., Puig, V., & Ocampo-Martinez, C. (2020). Characterization of interval-observer fault detection and isolation properties using the set-invariance approach. Journal of the Franklin Institute, 357(3), 1853-1886. https://doi.org/10.1016/j.jfranklin.2019.11.027
Pourbabaee, B., Meskin, N., & Khorasani, K. (2016). Sensor fault detection, isolation, and identification using multiple-model-based hybrid Kalman filter for gas turbine engines. IEEE Transactions on Control Systems Technology, 24(4), 1184-1200. https://doi.org/10.1109/TCST.2015.2480003
Rasoolzadeh, A., & Salmasi, F. R. (2020). Mitigating zero dynamic attacks in communication link-enabled droop-controlled hybrid AC/DC microgrids. IET Cyber-Physical Systems: Theory & Applications, 5(2), 207-217. https://doi.org/10.1049/iet-cps.2019.0043
Sun, B., Wang, J., He, Z., Qin, Y., Wang, D., & Zhou, H. (2019). Fault detection for closed-loop control systems based on parity space transformation. IEEE Access, 7, 75153-75165. https://doi.org/10.1109/ACCESS.2019.2916785
Tripathi, R. P., Ghosh, S., & Chandle, J. O. (2016). Tracking of object using optimal adaptive Kalman filter. In 2016 IEEE International Conference on Engineering and Technology (ICETECH) (pp. 1128-1131). IEEE Publishing. https://doi.org/10.1109/ICETECH.2016.7569426
Wang, Y., Liu, Q., Li, K., Yin, L., & Chen, H. (2019). Resilient fault and attack detection of DCT vehicles using parity space approach. In 2019 Chinese Automation Congress (CAC) (pp. 431-436). IEEE Publishing. https://doi.org/10.1109/CAC48633.2019.8996359
Willsky, A., & Jones, H. (1976). A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems. IEEE Transactions on Automatic Control, 21(1), 108-112. https://doi.org/10.1109/TAC.1976.1101146
Wünnenberg, J., & Frank, P. M. (1987). Sensor fault detection via robust observers. In S. Tzafestas, M. Singh & G. Schmidt (Eds.), System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches (pp. 147-160). Springer. https://doi.org/10.1007/978-94-009-3929-5_5
Yang, X., Chen, Y., Li, B., & Luo, D. (2020). Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model. Energy, 191, Article 116509. https://doi.org/10.1016/j.energy.2019.116509
Ye, H., Wang, W., & Zhai, S. (2015). Fault diagnosis based on parameter estimation in closed-loop systems. IET Control Theory & Applications, 9(7), 1146-1153. https://doi.org/10.1049/iet-cta.2014.0717
Zammali, C., Van Gorp, J., Wang, Z., & Raïssi, T. (2020). Sensor fault detection for switched systems using interval observer with L∞ performance. European Journal of Control, 57, 147-156. https://doi.org/10.1016/j.ejcon.2020.06.004
Zhang, K., Jiang, B., Yan, X. G., & Mao, Z. (2017). Incipient sensor fault estimation and accommodation for inverter devices in electric railway traction systems. International Journal of Adaptive Control and Signal Processing, 31(5), 785-804. https://doi.org/10.1002/acs.2730
Zhang, P., Ye, H., Ding, S. X., Wang, G. Z., & Zhou, D. H. (2006). On the relationship between parity space and approaches to fault detection. Systems & Control Letters, 55(2), 94-100. https://doi.org/10.1016/j.sysconle.2005.05.006
Zhang, Y., & Jiang, J. (2008). Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control, 32(2), 229-252. https://doi.org/10.1016/j.arcontrol.2008.03.008
Zhang, Z. H., Li, S., Yan, H., & Fan, Q. Y. (2019). Sliding mode switching observer-based actuator fault detection and isolation for a class of uncertain systems. Nonlinear Analysis: Hybrid Systems, 33, 322-335. https://doi.org/10.1016/j.nahs.2019.04.001
Zhirabok, A. N., Shumsky, A. E., & Zuev, A. V. (2018). Sliding mode observers for fault detection in linear dynamic systems. IFAC-PapersOnLine, 51(24), 1403-1408. https://doi.org/10.1016/j.ifacol.2018.09.540
Zhong, M., Ding, S. X., Lam, J., & Wang, H. (2003). An LMI approach to design robust fault detection filters for uncertain LTI systems. Automatica, 39(3), 543-550. https://doi.org/10.1016/S0005-1098(02)00269-8
Zhong, M., Song, Y., Xue, T., Yang, R., & Li, W. (2018). Parity space-based fault detection by minimum error minimax probability machine. IFAC-PapersOnLine, 51(24), 1292-1297. https://doi.org/10.1016/j.ifacol.2018.09.568
Zhou, J., & Zhang, D. (2019). H-infinity fault detection for delta operator systems with random two-channels packet losses and limited communication. IEEE Access, 7, 94448-94459. https://doi.org/10.1109/ACCESS.2019.2928306
Zhu, Y., & Gao, Z. (2014). Robust observer-based fault detection via evolutionary optimization with applications to wind turbine systems. In 9th IEEE Conference on Industrial Electronics and Applications (pp. 1627-1632). IEEE Publishing. https://doi.org/10.1109/ICIEA.2014.6931428
ISSN 0128-7680
e-ISSN 2231-8526