Sliding mode fuzzy observers and neural networks applied to solve fault detection and isolation problem

In this paper, non-linear systems (hydraulic tank configurations) were analyzed using a technique of fault detection and isolation based on a Takagi-Sugeno fuzzy model. The system state vector was obtained by means of sliding mode observers, and then the signal-residual is generated by comparing the estimated and measured outputs. The isolation problem was solved using Neural Networks. From the resulting active or inactive signal-residuals, the faulted elements of the system are easily identified. The method proposed represents a hybrid fault diagnosis technique.

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

[2]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[3]  Karsten P. Ulland,et al.  Vii. References , 2022 .

[4]  Kazuo Tanaka,et al.  Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach , 2008 .

[5]  Alessandro De Luca,et al.  Nonlinear Fault Detection and Isolation in a Three-Tank Heating System , 2006, IEEE Transactions on Control Systems Technology.

[6]  Christopher Edwards,et al.  Sliding mode control : theory and applications , 1998 .

[7]  Belkacem Ould Bouamama,et al.  Bond Graph Approach for Plant Fault Detection and Isolation: Application to Intelligent Autonomous Vehicle , 2014, IEEE Transactions on Automation Science and Engineering.

[8]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[9]  B. Castillo-Toledo,et al.  Model-Based Fault Diagnosis Using Sliding Mode Observers to Takagi-Sugeno Fuzzy Model , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

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

[11]  N. Wada,et al.  Development and implementation of a power system fault diagnosis expert system , 1995 .

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

[13]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[14]  K. Khorasani,et al.  A Neural Network-Based Multiplicative Actuator Fault Detection and Isolation of Nonlinear Systems , 2013, IEEE Transactions on Control Systems Technology.

[15]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[16]  Zhi-Hua Zhou,et al.  Using neural networks for fault diagnosis , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[17]  Suttichai Premrudeepreechacharn,et al.  Induction motor fault detection and diagnosis using supervised and unsupervised neural networks , 2002, 2002 IEEE International Conference on Industrial Technology, 2002. IEEE ICIT '02..

[18]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .