Fusion of qualitative bond graph and genetic algorithms: a fault diagnosis application.

In this paper, the problem of fault diagnosis via integration of genetic algorithms (GA's) and qualitative bond graphs (QBG's) is addressed. We suggest that GA's can be used to search for possible fault components among a system of qualitative equations. The QBG is adopted as the modeling scheme to generate a set of qualitative equations. The qualitative bond graph provides a unified approach for modeling engineering systems, in particular, mechatronic systems. In order to demonstrate the performance of the proposed algorithm, we have tested the proposed algorithm on an in-house designed and built floating disc experimental setup. Results from fault diagnosis in the floating disc system are presented and discussed. Additional measurements will be required to localize the fault when more than one fault candidate is inferred. Fault diagnosis is activated by a fault detection mechanism when a discrepancy between measured abnormal behavior and predicted system behavior is observed. The fault detection mechanism is not presented here.

[1]  Mourad Elhadef,et al.  An evolutionary algorithm for identifying faults in t-diagnosable systems , 2000, Proceedings 19th IEEE Symposium on Reliable Distributed Systems SRDS-2000.

[2]  Dean Karnopp,et al.  A Definition of the Bond Graph Language , 1972 .

[3]  Derek A. Linkens,et al.  Intelligent Supervisory Control - A Qualitative Bond Graph Reasoning Approach , 1996, World Scientific Series in Robotics and Intelligent Systems.

[4]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[5]  L. P. Khoo,et al.  A fuzzy-based genetic approach to the diagnosis of manufacturing systems , 2000 .

[6]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[7]  Olivier Raiman,et al.  Order of Magnitude Reasoning , 1986, Artif. Intell..

[8]  D. A. Linkens,et al.  Fault diagnosis based on a qualitative bond graph model, with emphasis on fault localisation , 1994 .

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  Pieter J. Mosterman,et al.  Diagnosis of continuous valued systems in transient operating regions , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Benjamin J. Kaipers,et al.  Qualitative Simulation , 1989, Artif. Intell..

[13]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[14]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[15]  Cristian Ghiaus.  Fault diagnosis of air conditioning systems based on qualitative bond graph , 1999 .

[16]  Johan de Kleer,et al.  A Qualitative Physics Based on Confluences , 1984, Artif. Intell..

[17]  Zhao Bingquan,et al.  Application of genetic algorithms to fault diagnosis in nuclear power plants , 2000 .

[18]  Brian Falkenhainer,et al.  Compositional Modeling: Finding the Right Model for the Job , 1991, Artif. Intell..

[19]  Leonard Bolc,et al.  Search methods for artificial intelligence , 1992 .

[20]  Ronald R. Yager,et al.  Essentials of fuzzy modeling and control , 1994 .

[21]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artif. Intell..

[22]  Forbes T. Brown,et al.  Engineering system dynamics : a unified graph-centered approach , 2006 .

[23]  Benjamin Kuipers,et al.  Qualitative reasoning: Modeling and simulation with incomplete knowledge , 1994, Autom..

[24]  C. H. Lie,et al.  Fault Tree Analysis, Methods, and Applications ߝ A Review , 1985, IEEE Transactions on Reliability.