Unknown Fault Diagnosis for Nonlinear Hybrid Systems Using Strong State Tracking Particle Filter

A strong state tracking particle filter (SST-PF) is put forward for unknown fault diagnosis of hybrid system. SST-PF overcomes the problem of sample impoverishment for tracking the state of nonlinear hybrid system by setting permanent transition probabilities from one mode to another. Meanwhile threshold logic of normalization factor based on the statistics is built to detect unknown-faults, which is more accurate and reasonable for tiny mode differences of hybrid system. Simulation experiments are carried out to analyze the effects of SST-PF, and it is shown that our algorithm has strong tracking ability for states and pretty detection ability for both known and unknown faults.

[1]  Zhuo-Hua Duan,et al.  Particle Filtering Algorithm for Fault Diagnosis of Multiple Model Hybrid Systems with Incomplete Models: Particle Filtering Algorithm for Fault Diagnosis of Multiple Model Hybrid Systems with Incomplete Models , 2008 .

[2]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[3]  Wang Chunping,et al.  Expert System for Radar Equipment Faults Diagnosis Based on Rough Set Theory , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[4]  Ian Jenkinson,et al.  Inference and learning methodology of belief-rule-based expert system for pipeline leak detection , 2007, Expert Syst. Appl..

[5]  Xuehong Sun,et al.  Hybrid System State Tracking and Fault Detection Using Particle Filters , 2006, IEEE Transactions on Control Systems Technology.

[6]  Wang Hong.  THE APPLICATION OF QUALITATIVE SIMULATION TO PROCESS MONITORING AND FAULT DIAGNOSIS OF BOILER , 2007 .

[7]  Dimitris A. Karras,et al.  Fault tree analysis and fuzzy expert systems: Early warning and emergency response of landfill operations , 2009, Environ. Model. Softw..

[8]  Visakan Kadirkamanathan,et al.  Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[9]  G.J. Vachtsevanos,et al.  A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine , 2007, 2007 Mediterranean Conference on Control & Automation.

[10]  Duan Zhuo.  Particle Filtering Algorithm for Fault Diagnosis of Multiple Model Hybrid Systems with Incomplete Models , 2008 .

[11]  Yutian Liu,et al.  Fault diagnosis and prediction of hybrid system based on particle filter algorithm , 2008, 2008 IEEE International Conference on Automation and Logistics.