Feature Extraction, Optimization and Classification by Second Generation Wavelet and Support Vector Machine for Fault Diagnosis of Water Hydraulic Power System

Abstract The work described in this paper investigates the fault diagnosis of water hydraulic motor by the optimization and automatic classification of the feature values. The second generation wavelet for the vibration signals analysis of the water hydraulic motor was proposed to extract the feature values. The new optimization method by bi-classification support vector machine (SVM) was proposed to select the optimal feature values based on a rank criterion and the algorithm was developed here. In order to classify the conditions of the pistons used in the hydraulic motor, a two-level structure based on the multi-classification was developed in this work. The multi-classification method of SVM for the fault diagnosis of a piston crack was investigated. The winner-takes-all scheme was studied. The results of the classification were found to be successful.

[1]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[2]  Hong Wang,et al.  Fault diagnosis in fluid power systems , 1996 .

[3]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[4]  W. Sweldens.  The Lifting Scheme: A Custom - Design Construction of Biorthogonal Wavelets "Industrial Mathematics , 1996 .

[5]  J Watton,et al.  An On-Line Approach to Fault Diagnosis of Fluid Power Cylinder Drive Systems , 1994 .

[6]  B Samanta,et al.  Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system , 2001 .

[7]  Duc Truong Pham,et al.  Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks , 1998 .

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

[9]  Clifford R. Burrows,et al.  Fault diagnosis of a hydraulic actuator circuit using neural networks—an output vector space classification approach , 1997 .

[10]  P W Ilott,et al.  Fault diagnosis of pumping machinery using artificial neural networks , 1997 .

[11]  Monika Ivantysynova,et al.  An Investigation into Micro- and Macrogeometric Design of Piston/Cylinder Assembly of Swash Plate Machines , 2004 .

[12]  P. Sørensen.  News and trends by the industrial application of water hydraulics , 1999 .

[13]  Patrick S. K. Chua,et al.  Dynamic vibration analysis of a swash-plate type water hydraulic motor , 2006 .

[14]  I. Daubechies,et al.  Factoring wavelet transforms into lifting steps , 1998 .