A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

Abstract A novel intelligent diagnosis model based on wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) for gearbox fault diagnosis is proposed. Wavelet support vector machine is a powerful novel tool for solving the diagnosis problem with small sampling, nonlinearity and high dimension. Immune genetic algorithm is developed in this study to determine the optimal parameters for WSVM with the highest accuracy and generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by empirical mode decomposition (EMD). The experimental results indicate that this proposed approach is an effective method for gearbox fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the artificial neural network and the SVM which has randomly extracted parameters.

[1]  Cheng Haozhong,et al.  Fault diagnosis of power transformer based on multi-layer SVM classifier , 2005 .

[2]  Aiguo Song,et al.  An immune evolutionary algorithm for sphericity error evaluation , 2004 .

[3]  E. P. de Moura,et al.  Applications of detrended-fluctuation analysis to gearbox fault diagnosis , 2009 .

[4]  Gregory Dudek,et al.  Auto-correlation wavelet support vector machine , 2009, Image Vis. Comput..

[5]  R. Bhuvaneswari,et al.  Artificial immune system for parameter estimation of induction motor , 2010, Expert Syst. Appl..

[6]  Qi Wu,et al.  The forecasting model based on wavelet nu-support vector machine , 2009, Expert Syst. Appl..

[7]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[8]  Xi Long,et al.  Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation , 2008, Image Vis. Comput..

[9]  Esin Dogantekin,et al.  An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier , 2011, Expert Syst. Appl..

[10]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[11]  Ling-Bing Tang,et al.  Forecasting volatility based on wavelet support vector machine , 2009, Expert Syst. Appl..

[12]  Bo-Suk Yang,et al.  Wavelet support vector machine for induction machine fault diagnosis based on transient current signal , 2008, Expert Syst. Appl..

[13]  Yang Yu,et al.  A roller bearing fault diagnosis method based on EMD energy entropy and ANN , 2006 .

[14]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[15]  Nicola Ancona,et al.  Ball detection in static images with Support Vector Machines for classification , 2003, Image Vis. Comput..

[16]  Ching Y. Suen,et al.  A trainable feature extractor for handwritten digit recognition , 2007, Pattern Recognit..

[17]  Liu Yang-guang,et al.  Wave impedance inversion in coalfield based on immune genetic algorithm , 2009 .

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Xiaoguang Hu,et al.  An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine , 2011 .

[20]  Xiangyang Wang,et al.  A wavelet-based image denoising using least squares support vector machine , 2010, Eng. Appl. Artif. Intell..

[21]  Richard Y. K. Fung,et al.  An immune-genetic algorithm for introduction planning of new products , 2009, Comput. Ind. Eng..

[22]  Min-Der Lin,et al.  Application of immune algorithms on solving minimum-cost problem of water distribution network , 2008, Math. Comput. Model..

[23]  Jia-wei Luo,et al.  Motif discovery using an immune genetic algorithm. , 2010, Journal of theoretical biology.

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

[25]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[26]  Amiya R Mohanty,et al.  Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform , 2008 .

[27]  Thomas Serre,et al.  Hierarchical classification and feature reduction for fast face detection with support vector machines , 2003, Pattern Recognit..

[28]  Fulei Chu,et al.  Support vector machines-based fault diagnosis for turbo-pump rotor , 2006 .

[29]  Fan Yang,et al.  Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker , 2011 .

[30]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[31]  Ahmad Ghasemloonia,et al.  Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis , 2011, Expert Syst. Appl..

[32]  Sheng-wei Fei,et al.  Fault diagnosis of power transformer based on support vector machine with genetic algorithm , 2009, Expert Syst. Appl..