Fault Diagnosis Based on Particle Filter for Hybrid System

Abstract

Abstract:

Abstract： Aimed at the problem of fault diagnosis in the hybrid system, a particle filter algorithm for hybrid system state estimation and discrete modal identification was proposed. The proposed algorithm was spread to joint estimation of state and parameters, modified Bayes algorithm was utilized to give fault judgment, and fault diagnosis of hybrid system was implemented. The results of the simulation of the twotank state estimation and fault diagnosis show that the proposed algorithm can not only accurately diagnose the hybrid system fault, maintain better state and parameter estimation accuracy when fault occurs, but also be used for adaptive filtering, reliability prediction, fault tolerant control and many other fields.

Key words: hybrid system, particle filter, fault diagnosis, state estimation, discrete modal identification

CLC Number:

TP 277

LI Xiongjie1,2，ZHOU Donghua2. Fault Diagnosis Based on Particle Filter for Hybrid System[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 849-854.

References

［1］Witsenhausen H S. A class of hybridstate continuoustime dynamic systems［J］. IEEE Trans on Automatic Control, 1966, 11(6): 665683.

［2］Cellier F E. Combined continuous/discrete system simulation by use of digital computer: Techniques and tools［M］. Zurich, Switzerland: Swiss Federal Institute of Technology, 1979.

［3］Mosterman P J. Biswas G. Building hybrid observers for complex dynamic systems using model abstractions［C］//Proc ICBGM’99, San Francisco, CA. 1999: 157162.

［4］Rezai M, Lawrence P D, Ito M R. Analysis of faults in hybrid systems by global Petri nets［C］//Proc of IEEE International Conference on Systems. Man and Cybernetics,1995: 22512256.

［5］Bemporad A. Mignone D, Morari M. Moving horizon estimation for hybrid systems and fault detection［C］//Proc of ACC’99, San Diego. CA, USA, 1999: 24712475.

［6］Hibey J L, Charalambous C D. Conditional densities for continuoustime nonlinear hybrid systems with applications to fault detection［J］. IEEE Trans on Automatic Control, 1998, 44(11): 21642169.

［7］Li X R, Yaakov B S. Multiplemodel estimation with variable structure［J］. Automatic Control, IEEE Transactions on, 1996，41(4): 478493.

［8］Zhao F, Koutsoukos X, Haussecker H, et al. Monitoring and fault diagnosis of hybrid systems［J］. IEEE Trans on SMC Part B: Cybernetics, 2005, 35(6): 793801.

［9］Lerner U, Parr R, Koller D, et al. Bayesian fault detection and diagnosis in dynamic systems［C］//Proc of the 17th National conference on Artificial Interlligence(AAAI). Austin. Texas, 2000: 531537.

［10］Sampath M, Sengupta R, Lafortune S, et al. Failure diagnosis using discreteevent models［J］. Control Systems Technology, IEEE Transactions on, 1996，4(2): 105124.

［11］李雄杰，周东华. 混杂系统故障诊断的强跟踪滤波器算法［J］. 计算机工程与应用，2009, 45(9): 233236.

LI Xiongjie, ZHOU Donghua. Fault diagnosis based on strong tracking filter for hybrid systems［J］. Computer Engineering and Applications, 2009, 45(9): 233236.

［12］王法胜, 鲁明羽, 赵清杰, 等. 粒子滤波算法［J］. 计算机学报，2014，37(8): 16791694.

WANG Fasheng, LU Mingyu, ZHAO Qingjie, et al. Particle filtering algorithm ［J］. Chinese Journal of Computer, 2014, 37(8): 16791694.

［13］曹洁, 李玉琴, 吴迪. 基于改进差分进化的高精度粒子滤波算法［J］. 上海交通大学学报,2014,48(12): 17141720.

CAO Jiea, Li Yuqina, WU Di. A high precision particle filter based on improved differential evolution ［J］. Journal of Shanghai Jiaotong University, 2014, 48(12): 17141720.

［14］万磊，杨勇，李岳明. 水下机器人执行器的高斯粒子滤波故障诊断方法［J］.上海交通大学学报,2013,47(7): 10721076.

WEN Lei, YANG Yong, LI Yueming. Actuator fault dignosis of automatic underwater vehicle using gaussian particle filter［J］. Journal of Shanghai Jiaotong University, 2013, 47(7): 10721076.

［15］LENG Hongze， SONG Junqiang. Hybrid threedimensional variation and particle filtering for nonlinear systems［J］. Chinese Physics B, 2013, 22(3): 030505.

［16］周东华，叶银忠. 现代故障诊断与容错控制［M］. 北京：清华大学出版社，2000：106109.

[1]	PAN Meiqi, HE Xing. A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning [J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 561-568.
[2]	XUE Guiting, LIU Zhe, HAN Zhaoru, SHI Fang, WANG Ti, WANG Xiao. Switching Modeling and Application in Fault Diagnosis Algorithm Testing of Distribution Network [J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1381-1389.
[3]	ZHANG Chunmei, XU Xingque, LIU Silin. Distribution Network Fault Diagnosis Technology Based on Multi-Source Data Fusion [J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 739-746.
[4]	CUI Xian, CHEN Ziqiang. A Highly Robust State of Health Estimation Method for Lithium-Ion Batteries Based on ECM and SGPR [J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 747-759.
[5]	ZANG Hongyan, XIE Xiaolong, XU Yazhou, TAO Ye, GAO Changsheng. Hypersonic Maneuvering Target State Estimation Algorithm Based on Recurrent Neural Network [J]. Air & Space Defense, 2024, 7(4): 88-98.
[6]	CHEN Kun(陈坤), ZHAO Xu(赵旭), DONG Chunyu(董春玉), DI Zichao(邸子超), CHEN Zongzhi(陈宗枝). Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 400-413.
[7]	SUN Zhiwei, HU Xiong, DONG Kai, SUN Dejian, LIU Yang. RUL Prediction Method for Quay Crane Hoisting Gearbox Bearing Based on LSTM-CAPF Framework [J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 352-360.
[8]	ZHANG Baolei1, WU Yuke2, TANG Yufeng2. Fault Diagnosis of Electric Slip Ring of EPSO Single Point Mooring Base on CART Decision Tree [J]. Ocean Engineering Equipment and Technology, 2024, 11(2): 95-100.
[9]	LAI Guoliang, HU Zhaozheng, ZHOU Zhe, WAN Jinjie, REN Jingyuan. Simultaneous Detection and Localization for Intelligent Vehicles Based on HD Map Matching and Semantic Likelihood Model [J]. Journal of Shanghai Jiao Tong University, 2024, 58(10): 1618-1628.
[10]	GUO Junfeng, WANG Miaosheng, WANG Zhiming. Fault Diagnosis of Rolling Bearing with Roller Spalling Based on Two-Step Transfer Learning on Unbalanced Dataset [J]. Journal of Shanghai Jiao Tong University, 2023, 57(11): 1512-1521.
[11]	LIU Jiahe, HU Pengwei, CHENG Hailong. Research on Condition-Based Maintenance of Complex System Based on Performance Degradation [J]. Air & Space Defense, 2023, 6(1): 56-62.
[12]	LIU Yuming, ZHAO Yong, DONG Zhengjian, WANG Ping, JI Yuqi. Formation Control Strategy of Multiple Mobile Robots Cooperative Operation Based on Backstepping Method [J]. Journal of Shanghai Jiao Tong University, 2023, 57(1): 103-115.
[13]	XU Chenhui, YU Fanghui, HE Defeng. Disturbance-Blocking-Based Distributed Receding Horizon Estimation of Flexible Joint Robots [J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 868-876.
[14]	XU Yong, CAI Yunze, SONG Lin. Review of Research on Condition Assessment of Nuclear Power Plant Equipment Based on Data-Driven [J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 267-278.
[15]	NIE Rui, WANG Hongru. Fault Diagnosis of UAV Formation Actuator Based on Neural Network Observer [J]. Air & Space Defense, 2022, 5(2): 32-41.