Bayesian based Fault Identification for Nonlinear Mechatronic System with Backlash

Abstract: This article attempts to solve the problem of fault identification of nonlinear mechatronic system with backlash. The fault detection and isolation are carried out by evaluating the residuals and the fault signature matrix derived from the bond graph model of the system. In order to refine the fault candidates set after fault isolation, a Bayesian method is adopted where the potential faults in the fault candidates set are treated as the special states to facilitate the unknown parameters estimation. According to the estimation results, the true faults can be obtained which are useful for further maintenance purpose. Simulation studies are conducted to validate the proposed method.

[1]  Bo-Suk Yang,et al.  Application of relevance vector machine and logistic regression for machine degradation assessment , 2010 .

[2]  M.I. Valla,et al.  Model Based Stator Fault Detection in Induction Motors , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[3]  Alan S. Perelson,et al.  System Dynamics: A Unified Approach , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Matthew Daigle,et al.  A Model-Based Prognostics Approach Applied to Pneumatic Valves , 2011 .

[5]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[6]  Danwei Wang,et al.  Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation , 2010, IEEE Transactions on Automation Science and Engineering.

[7]  António J. Marques Cardoso,et al.  Open-Circuit Fault Diagnosis in PMSG Drives for Wind Turbine Applications , 2013, IEEE Transactions on Industrial Electronics.

[8]  Bin Zhang,et al.  Application of Blind Deconvolution Denoising in Failure Prognosis , 2009, IEEE Transactions on Instrumentation and Measurement.

[9]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[10]  Belkacem Ould Bouamama,et al.  Robust Monitoring of an Electric Vehicle With Structured and Unstructured Uncertainties , 2009, IEEE Transactions on Vehicular Technology.

[11]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[12]  Danwei Wang,et al.  Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives , 2010, IEEE Transactions on Automation Science and Engineering.

[13]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[14]  Geneviève Dauphin-Tanguy,et al.  Bond graph models of structured parameter uncertainties , 2005, J. Frankl. Inst..

[15]  Danwei Wang,et al.  Fault Detection Isolation and Estimation in a Vehicle Steering System , 2012, IEEE Transactions on Industrial Electronics.