Fault Diagnosis of Nonlinear Systems Using Dynamic Neural Networks
This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPE) associated with a set of singleparameter fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. Two NPE structures including series-parallel and parallel are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the NPEs to systems with partial-state measurement.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100671Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1930
 S. Simani, C. Fantuzzi, R. J. Patton, Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, Springer 2003.
 R. Isermann, Model-based Fault Detection and Diagnosis - Status and Applications, Annual Reviews in Control 29 (2005) 71-85.
 R. Rengaswamy, D. Mylaraswamy, V. Venkatasubramanian, K. E. Arzen, A Comparison of Model-based and Neural Network-based Diagnostic Methods, Engineering Applications of Artificial Intelligence 14 (2001) 808-818.
 P. Frank, Fault Diagnosis in Dynamic Systems using Analytical and Knowledge-based Redundancy: A Survey and Some New Results, Automatica 26 (1990) 459-474.
 R. J. Patton, C.J. Lopez-Toribio, F. J. Uppal, Artificial Intelligence Approaches to Fault Diagnosis,” in: IEE Colloquium on Condition Monitoring: Machinery, External Structures and Health (Ref. No. 1999/034), April 1999, pp. 5/I-5/18.
 N. Vaswani, Adaptive Change Detection in Nonlinear Systems with Unknown Change Parameters, IEEE Trans. on Signal Processing 55 (3) (2007) 859-872.
 P. Li, V. Kadirkamanathan, Particle Filtering based Likelihood Ratio Approach to Fault Diagnosis in Nonlinear Stochastic Systems, IEEE Trans. on Sys, Man Cybernetics 31 (3) (2001) 337-343.
 P. Li, V. Kadirkamanathan, Fault Detection and Isolation in Non-linear Stochastic Systems - A Combined Adaptive Monte Carlo Filtering and Likelihood Ratio Approach, Int. J. of Control 77 (12) (2004) 1101-1114.
 L. Guo, L. Yin, H. Wang, T Chai, Entropy Optimization Filtering for Fault Isolation of Nonlinear Non-Gaussian Stochastic Systems, IEEE Trans. on Automatic Control 54 (4) (2009) 804-810.
 E. Sobhani-Tehrani, K. Khorasani, S. Tafazoli, Dynamic Neural Network-based Estimator for Fault Diagnosis in Reaction Wheel Actuator of Satellite Attitude Control System, in: Proceedings of the IEEE Int. Joint Conf. on Neural Networks (IJCNN), Montreal, Canada, 2005, pp. 2347-2352.
 A. Alessandri, Fault Diagnosis for Nonlinear Systems using a Bank of Neural Estimators, Computers in industry 52 (2003) 271-289.
 Z. Xiaodong, M. M. Polycarpou, T. Parisini, A Robust Detection and Isolation Scheme for Abrupt and Incipient Faults in Nonlinear Systems, IEEE Trans. on Auto. Control 47 (4) (2002) 576 – 593.
 M. M. Polycarpou, A. J. Helmicki, Automated Fault Detection and Accommodation: A Learning Systems Approach, IEEE Trans. on Sys. Man Cybernetics 25 (11) (1995) 1447 – 1458.
 R. K. Mehra, C. Rago, S. Seereeram, Failure Detection and Identification using a Nonlinear Interactive Multiple Model (IMM) Filtering Approach with Aerospace Applications, in: Proceedings of the 11th IFAC Symposium on System Identification, Fukuoka, Japan, July 1997.
 Y. Zhang, Xiao, Detection and Diagnosis of Sensor and Actuator Failures using Interacting Multiple-Model Estimator, in: Proceedings of the 36th IEEE Conference on Decision and Control, San Diego, CA, Dec. 1997, pp. 4475-4480.
 T. Jiang, K. Khorasani, S. Tafazoli, Parameter Estimation-Based Fault Detection, Isolation and Recovery for Nonlinear Satellite Models, IEEE Transactions on Control Systems Technology 16 (4) (2008) 799-808.
 N. Tudoroiu, K. Khorasani, Fault Detection and Diagnosis for Satellite’s Attitude Control System using An Interactive Multiple Model (IMM) Approach, in: Proceedings of the 2005 Conference on Control Applications, Toronto, Canada, August 2005, pp. 1287-1292.
 N. Tudoroiu, E. Sobhani-Tehrani, K. Khorasani, Interactive Bank of Unscented Kalman Filters for Fault Detection and Isolation in Reaction Wheel Actuators of Satellite Attitude Control System, in: Proceedings of the 32nd IEEE Conference on Industrial Electronics, Paris, France, 2006, pp. 264-269.
 C. Rago, R. Pransanth., R. K. Mehra, R. Fortenbaugh, Failure Detection and Identification and Fault Tolerant Control using the IMM-KF with Applications to the Eagle-Eye UAV, in: 37th IEEE Conf. on Decision and Control, Tampa, FL, December 1998, vol. 4, pp. 4208-4213.
 A. Medvedev, State Estimation and Fault Detection by a Bank of Continuous Finite-Memory Filters, Int. J. of Control 69 (4) (1998) 499- 517.
 R. Isermann, Fault Diagnosis of Machines via Parameter Estimation and Knowledge Processing - A Tutorial Paper, Automatica 29 (4) (1994) 815-835.
 A. Houacine, Regularized Fast Recursive-Least Squares Algorithms for Finite Memory Filtering, IEEE Transactions on Signal Processing 40 (1992) 758-769.
 S. Haykin, Kalman filters, in: S. Haykin (Ed.), Kalman Filtering and Neural Networks, Wiley/Interscience, 2001, pp. 1-22.
 T. Parisini, R. Zoppoli, Neural Networks for Nonlinear State Estimation, International Journal of Robust Control 4 (1994) 231-248.
 J. T. Lo, Synthetic Approach to Optimal Filtering, IEEE Transactions on Neural Networks 5 (5) (1994) 803-811.
 S. W. Piche, Steepest Descent Algorithms for Neural Network Controller and Filters, IEEE Transactions on Neural Networks 5 (2) (1994) 198-212.
 N. Tudoroiu, K. Khorasani, Fault Detection and Diagnosis for Reaction Wheels of Satellite’s Attitude Control System using a Bank of Kalman Filters, in: Int. Symp. on Signals, Circuits and Systems, Iasi, Romania, vol. 1, July 2005, pp. 199-202.
 H. A. Talebi, R.V. Patel, An Intelligent Fault Detection and Recovery Scheme for Reaction Wheel Actuator of Satellite Attitude Control Systems, in: IEEE Conf. on Control Applications, Munich, Germany, October 2006, pp. 3282 – 3287.
 H. A. Talebi, K. Khorasani, S. Tafazoli, A Recurrent Neural Networkbased Sensor and Actuator Fault Detection, Isolation for Nonlinear Systems with Application to a Satellite's Attitude Control Subsystem, IEEE Transactions on Neural Networks 20 (1) (2009) 45-60.
 A. Valdes, K. Khorasani, A Pulsed Plasma Thruster Fault Detection and Isolation Strategy for Formation Flying Satellites, Applied Soft Computing 10 (3) (2010) 746-758.