A Probabilistic Finite State Automata-based Fault Detection Method for Traction Motor

Traction motor plays an important role in the reliable operation of high-speed train, so it is necessary to detect the fault of traction motor. The traction system of high-speed train is complex and the operating conditions are variable, resulting in different fault severity and fault type, which is difficult to detect. And, most of the fault detection methods are based on the time series of traction motor, and few are based on the symbol sequence. This paper presents a probabilistic finite state automata (PFSA)-based fault detection method for traction motor of high-speed trains. Three-phase current of traction motor is first converted into a symbol sequence via symbolic aggregate approximation (SAX). PFSA model is constructed to represent the symbol sequence, and state transitions in the model are described through D-Markov machine. The state transition counting matrix of PFSA model is used to detect the occurrence of faults. The proposed method is tested on a benchmark of the traction motor of a high-speed train. Through the simulation results, it is verified the effectiveness of the proposed method.

[1]  Weihua Gui,et al.  A Fault-Injection Strategy for Traction Drive Control Systems , 2017, IEEE Transactions on Industrial Electronics.

[2]  Soumik Sarkar,et al.  Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video , 2015, CoCo@NIPS.

[3]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[4]  C. Finney,et al.  A review of symbolic analysis of experimental data , 2003 .

[5]  Steven X. Ding,et al.  Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms , 2018, IEEE Transactions on Industrial Electronics.

[6]  Zhiwei Gao,et al.  From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.

[7]  Weihua Gui,et al.  Hardware-in-the-Loop Fault Injection for Traction Control System , 2018, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[8]  Pierre Dupont,et al.  Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms , 2005, Pattern Recognit..

[9]  Zhiwen Chen,et al.  Current Characteristics Analysis and Fault Injection of an Early Weak Fault in Broken Rotor Bar of Traction Motor , 2018, Mathematical Problems in Engineering.

[10]  Chao Liu,et al.  An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems , 2019, Mechanical Systems and Signal Processing.

[11]  Kushal Mukherjee,et al.  State splitting and merging in probabilistic finite state automata for signal representation and analysis , 2014, Signal Process..

[12]  Haitham Abu-Rub,et al.  Speed and Load Torque Observer Application in High-Speed Train Electric Drive , 2010, IEEE Transactions on Industrial Electronics.

[13]  Douglas Lind,et al.  An Introduction to Symbolic Dynamics and Coding , 1995 .

[14]  Asok Ray,et al.  Symbolic dynamic analysis of complex systems for anomaly detection , 2004, Signal Process..

[15]  Chia-Lin Chen,et al.  Reshaping Chinese space-economy through high-speed trains: opportunities and challenges , 2012 .

[16]  Gojko Joksimovic,et al.  The detection of inter-turn short circuits in the stator windings of operating motors , 2000, IEEE Trans. Ind. Electron..

[17]  Weihua Gui,et al.  A data-driven ground fault detection and isolation method for main circuit in railway electrical traction system. , 2019, ISA transactions.

[18]  Steven X. Ding,et al.  Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .

[19]  António J. Marques Cardoso,et al.  Airgap-Eccentricity Fault Diagnosis, in Three-Phase Induction Motors, by the Complex Apparent Power Signature Analysis , 2008, IEEE Transactions on Industrial Electronics.

[20]  Stéphane Ploix,et al.  Fault diagnosis and fault tolerant control , 2007 .

[21]  Asok Ray,et al.  Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns , 2009, 2008 American Control Conference.

[22]  Francisco Casacuberta,et al.  Probabilistic finite-state machines - part I , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..