Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

[1]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[2]  Venkat Venkatasubramanian,et al.  Model-based reasoning in diagnostic expert systems for chemical process plants , 1987 .

[3]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[4]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[5]  Michael I. Jordan.  Graphical Models , 1998 .

[6]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[7]  Robert Milne,et al.  Strategies for Diagnosis , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Marcelo Godoy Simões,et al.  A Bayesian network fault diagnostic system for proton exchange membrane fuel cells , 2007 .

[9]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[10]  Mohamed Benbouzid,et al.  Application of fuzzy logic to induction motors condition monitoring , 1999 .

[11]  Sheila A. McIlraith,et al.  Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net , 2002, UAI.

[12]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[13]  Y. S. Lin,et al.  Using Bayesian Network for Fault Location on Distribution Feeder , 2002, IEEE Power Engineering Review.

[14]  Xiao Zhi Gao,et al.  Soft computing methods in motor fault diagnosis , 2001, Appl. Soft Comput..

[15]  Venkat Venkatasubramanian,et al.  Causality‐based failure‐driven learning in diagnostic expert systems , 1989 .

[16]  Mohamed Benbouzid,et al.  A simple fuzzy logic approach for induction motors stator condition monitoring , 2001, IEMDC 2001. IEEE International Electric Machines and Drives Conference (Cat. No.01EX485).

[17]  Stephen Ogaji,et al.  Gas-turbine fault diagnostics: a fuzzy-logic approach , 2005 .