论文信息 - Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems

Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems

Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.

Sriram Narasimhan | Richard Dearden | Emmanuel Benazera

[1] Frank Hutter,et al. The Gaussian Particle Filter for Diagnosis of Non-Linear Systems , 2003 .

[2] S. Narasimhan,et al. Explanation constraint programming for model-based diagnosis of engineered systems , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[3] Eric J. Manders,et al. FDI of abrupt faults with combined statistical detection and estimation and qualitative fault isolation , 2003 .

[4] Paul M. Frank,et al. Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[5] Simon Haykin,et al. Special Issue on Sequential State Estimation , 2004, Proc. IEEE.

[6] B. Williams,et al. Multi-Modal Particle Filtering for Hybrid Systems with Autonomous Mode Transitions , 2003 .

[7] Brian C. Williams,et al. Mode Estimation of Probabilistic Hybrid Systems , 2002, HSCC.

[8] P. Pandurang Nayak,et al. A Model-Based Approach to Reactive Self-Configuring Systems , 1996, AAAI/IAAI, Vol. 2.

[9] Nando de Freitas,et al. Diagnosis by a waiter and a Mars explorer , 2004, Proceedings of the IEEE.

[10] Gabor Karsai,et al. A robust method for hybrid diagnosis of complex systems , 2003 .

[11] Rudolph van der Merwe,et al. The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).