Decentralized Fault Diagnosis and Prognosis Scheme for Interconnected Nonlinear Discrete-Time Systems
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Published
Nov 16, 2020
H. Ferdowsi
S. Jagannathan
Abstract
This paper deals with the design of a decentralized fault diagnosis and prognosis scheme for interconnected nonlinear discrete-time systems which are modelled as the interconnection of several subsystems. For each subsystem, a local fault detector (LFD) is designed based on the dynamic model of the local subsystem and the local states. Each LFD consists of an observer with an online neural network (NN)-based approximator. The online NN approximators only use local measurements as their inputs, and are always turned on and continuously learn the interconnection as well as possible fault function. A fault is detected by comparing the output of each online NN approximator with a predefined threshold instead of using the residual. Derivation of robust detection thresholds and fault detectability conditions are also included. Due to interconnected nature of the overall system, the effect of faults propagate to other subsystems, thus a fault might be detected in more than one subsystem. Upon detection, faults local to the subsystem and from other subsystems are isolated by using a central fault isolation unit which receives detection time information from all LFDs. The proposed scheme also provides the time-to-failure or remaining useful life information by using local measurements. Simulation results provide the effectiveness of the proposed decentralized fault detection scheme.
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Keywords
Fault Detection, Fault diagnostics, Failure Prediction, Interconnected Systems, Decentralized Systems, Adaptive Estimation
References
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Ferdowsi, H., Raja, D. L., & Jagannathan, S. (2012a). A decentralized fault prognosis scheme for nonlinear interconnected discrete-time systems. 2012 American Control Conference (ACC), 5900-5905.
Ferdowsi, H., Raja, D. L., & Jagannathan, S. (2012b). A decentralized fault detection and prediction scheme for nonlinear interconnected continuous-time systems. Neural Networks (IJCNN), The 2012 International Joint Conference on, 1-7.
Ferrari, R., Parisini, T., & Polycarpou, M. M. (2009). Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach. Automatic Control, IEEE Transactions on, 54(4), 794-799.
Golub, G. H., & Loan, C. F. V. (1996). Matrix computations (3rd ed.): Johns Hopkins University Press.
Huang, S. N., Tan, K. K., & Lee, T. H. (2005). Decentralized control of a class of large-scale nonlinear systems using neural networks. Automatica, 41(9), 1645-1649. doi: 10.1016/j.automatica.2005.02.010
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Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, K. (2014). Validation of model-based prognostics for pneumatic valves in a demonstration testbed. Annual Conference of the Prognostics
and Health Management Society, 76-85.
Luo, J., Namburu, M., Pattipati, K., Qiao, L., Kawamoto, M., & Chigusa, S. A. C. S. (2003). Model-based prognostic techniques [maintenance applications]. In AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference. Proceedings, 330-340.
Maki, Y., & Loparo, K. A. (1997). A neural-network approach to fault detection and diagnosis in industrial processes. Control Systems Technology, IEEE Transactions on, 5(6), 529-541. doi:
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Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555-1575.
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Thumati, B. T., & Jagannathan, S. (2010). A Model-Based Fault- Detection and Prediction Scheme for Nonlinear Multivariable Discrete-Time Systems With Asymptotic Stability Guarantees.
Neural Networks, IEEE Transactions on, 21(3), 404-423. doi: 10.1109/tnn.2009.2037498
Wang, H., & Daley, S. (1996). Actuator fault diagnosis: an adaptive observer-based technique. Automatic Control, IEEE Transactions on, 41(7), 1073-1078. doi: 10.1109/9.508919
Yan, X. G., & Edwards, C. (2008). Robust decentralized actuator fault detection and estimation for large-scale systems using a sliding mode observer. International Journal of control, 81(4), 591-606.
Zhang, J., & Morris, A. J. (1994). On-line process fault diagnosis using fuzzy neural networks. Intelligent Systems Engineering, 3(1), 37-47.
Blake, D., & Brown, M. (2007). Simultaneous, Multiplicative Actuator and Sensor Fault Estimation using Fuzzy Observers. Paper presented at the Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International.
Boskovic, J. D., & Mehra, R. K. (2002). A decentralized scheme for accommodation of multiple simultaneous actuator failures. Paper presented at the American Control Conference, 2002.
Proceedings of the 2002.
Bregon, A., Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos, X., & Pulido, B. (2014). An event-based distributed diagnosis framework using structural model decomposition. Artificial
Intelligence, 210, 1-35.
Caccavale, F., & Villani, L. (2004, June 30 2004-July 2 2004). An adaptive observer for fault diagnosis in nonlinear discrete-time systems. Paper presented at the American Control Conference,
2004. Proceedings of the 2004.
Dash, S., & Venkatasubramanian, V. (2000). Challenges in the industrial applications of fault diagnostic systems. Computers & Chemical Engineering, 24(2–7), 785-791. doi: 10.1016/s0098-1354(00)00374-4
Demetriou, M. A., & Polycarpou, M. M. (1998). Incipient fault diagnosis of dynamical systems using online approximators. Automatic Control, IEEE Transactions on, 43(11), 1612-1617. doi: 10.1109/9.728881
Daigle, M., & Goebel, K. (2009). Model-based prognostics with fixed-lag particle filters. Annual Conference of the Prognostics and Health Management Society, San Diego, CA.
Feldman, A., Kurtoglu, T., Narasimhan, S., Poll, S., Garcia, D., de Kleer, J., Kuhn, L., & van Gemund, A. (2010). Empirical evaluation of diagnostic algorithm performance using a generic
framework. International Journal of Prognostics and Health Management, 1(2), 1-28.
Ferdowsi, H., & Jagannathan, S. (2013). A unified model-based fault diagnosis scheme for non-linear discrete-time systems with additive and multiplicative faults. Transactions of the Institute of Measurement and Control, 35(6), 742-752. doi: 10.1177/0142331212473141
Ferdowsi, H., & Jagannathan, S. (2017). Decentralized fault tolerant control of a class of nonlinear interconnected systems. International Journal of Control, Automation and Systems, 15(2), 527-536.
Ferdowsi, H., Raja, D. L., & Jagannathan, S. (2012a). A decentralized fault prognosis scheme for nonlinear interconnected discrete-time systems. 2012 American Control Conference (ACC), 5900-5905.
Ferdowsi, H., Raja, D. L., & Jagannathan, S. (2012b). A decentralized fault detection and prediction scheme for nonlinear interconnected continuous-time systems. Neural Networks (IJCNN), The 2012 International Joint Conference on, 1-7.
Ferrari, R., Parisini, T., & Polycarpou, M. M. (2009). Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach. Automatic Control, IEEE Transactions on, 54(4), 794-799.
Golub, G. H., & Loan, C. F. V. (1996). Matrix computations (3rd ed.): Johns Hopkins University Press.
Huang, S. N., Tan, K. K., & Lee, T. H. (2005). Decentralized control of a class of large-scale nonlinear systems using neural networks. Automatica, 41(9), 1645-1649. doi: 10.1016/j.automatica.2005.02.010
Huang, S. N., Tan, K. K., & Lee, T. H. (2006). Nonlinear adaptive control of interconnected systems using neural networks. Neural Networks, IEEE Transactions on, 17(1), 243-246. doi: 10.1109/tnn.2005.857948
Isermann, R. (2005). Model-based fault-detection and diagnosis – status and applications. Annual Reviews in Control, 29(1), 71-85. doi: 10.1016/j.arcontrol.2004.12.002
Jagannathan, S. (2006). Neural Network Control of Nonlinear Discrete –time Systems. NY: CRC publications.
Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, K. (2014). Validation of model-based prognostics for pneumatic valves in a demonstration testbed. Annual Conference of the Prognostics
and Health Management Society, 76-85.
Luo, J., Namburu, M., Pattipati, K., Qiao, L., Kawamoto, M., & Chigusa, S. A. C. S. (2003). Model-based prognostic techniques [maintenance applications]. In AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference. Proceedings, 330-340.
Maki, Y., & Loparo, K. A. (1997). A neural-network approach to fault detection and diagnosis in industrial processes. Control Systems Technology, IEEE Transactions on, 5(6), 529-541. doi:
10.1109/87.641399
Patton, R. J., Chen, J., & Lopez-Toribio, C. J. (1998, 1998). Fuzzy observers for nonlinear dynamic systems fault diagnosis. Paper presented at the Decision and Control, 1998. Proceedings of the 37th IEEE Conference on.
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555-1575.
Stankovic, S., Ilic, N., Djurovic, Z., Stankovic, M., & Johansson, K. H. (2010, 6-8 Oct. 2010). Consensus based overlapping decentralized fault detection and isolation. Paper presented at the Control and Fault-Tolerant Systems (SysTol), 2010 Conference on.
Thumati, B. T., & Jagannathan, S. (2010). A Model-Based Fault- Detection and Prediction Scheme for Nonlinear Multivariable Discrete-Time Systems With Asymptotic Stability Guarantees.
Neural Networks, IEEE Transactions on, 21(3), 404-423. doi: 10.1109/tnn.2009.2037498
Wang, H., & Daley, S. (1996). Actuator fault diagnosis: an adaptive observer-based technique. Automatic Control, IEEE Transactions on, 41(7), 1073-1078. doi: 10.1109/9.508919
Yan, X. G., & Edwards, C. (2008). Robust decentralized actuator fault detection and estimation for large-scale systems using a sliding mode observer. International Journal of control, 81(4), 591-606.
Zhang, J., & Morris, A. J. (1994). On-line process fault diagnosis using fuzzy neural networks. Intelligent Systems Engineering, 3(1), 37-47.
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