Actuated Hydraulic System Fault Detection: A Fuzzy Logic Approach

Accurate detection of fault in a hydraulic system is a crucial and equally challenging task. A fuzzy logic topology is developed for the diagnosis of simulated faults in hydraulic power systems. The method proposed is a combination of analytical and fuzzy logic approach. Residuals generated by nonlinear observer are evaluated using fuzzy logic. The fault severity of the system is evaluated based on the membership functions and rule base developed by the fuzzy logic system. This paper demonstrates the use of fuzzy logic as an extension to analytical system to enhance the overall performance of the system. The decision of whether 'a fault has occurred or not?' is upgraded to 'what is the severity of that fault?' at the output. Simulation results showed that fuzzy logic is more sensitive and informative regarding the fault condition, and less sensitive to uncertainties and disturbances.

[1]  Chee Peng Lim,et al.  Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant , 2004 .

[2]  Guangyuan Liu,et al.  Discrete-time Robust Fault Detection Observer design: A genetic algorithm approach , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[3]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[4]  Nariman Sepehri,et al.  Nonlinear observer-based fault detection technique for electro-hydraulic servo-positioning systems , 2005 .

[5]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[6]  Majura F. Selekwa,et al.  Robust fault detection using robust l/sub 1/ estimation and fuzzy logic , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[7]  Shing Chiang Tan,et al.  Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant , 2004, IEEE Transactions on Energy Conversion.

[8]  Stephen L. Chiu,et al.  Perspectives on the industrial application of intelligent control , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[9]  Antero Arkkio,et al.  Detection of stator winding fault in induction motor using fuzzy logic , 2008, Appl. Soft Comput..

[10]  X. Z. Gao,et al.  State-of-the-art in soft computing-based motor fault diagnosis , 2003, Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003..

[11]  Paul M. Frank,et al.  Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation , 1996, IEEE Trans. Control. Syst. Technol..

[12]  Hai-bin Yu,et al.  Modified Morlet wavelet neural networks for fault detection , 2005, 2005 International Conference on Control and Automation.

[13]  Seraphin C. Abou,et al.  Fault Detection in Hydraulic System Using Fuzzy Logic , 2009 .