Fault diagnosis based on Walsh transform and support vector machine

Recognition of shaft orbit plays an important role in the fault diagnosis. In this paper, a novel recognition method for the shaft orbit based on Walsh transform and support vector machine is proposed. In the method, distance vector between the point on the shaft orbit and its center is first calculated. Then, the distance vector is transformed by Walsh matrix, and the Walsh spectrum obtained has property of invariance to rotation, scaling and translation. In the end, the Walsh spectrum, viewed as the feature of shaft orbit, is trained and tested by means of support vector machine. In addition, a comparison with the previous methods is performed, and experimental results are encouraging, which fully demonstrates the effectiveness and superiority of the proposed approach.

[1]  Jung Liu,et al.  Shape-based image retrieval using support vector machines, Fourier descriptors and self-organizing maps , 2007, Inf. Sci..

[2]  Qingmou Li,et al.  VisualAnomaly: A GIS-based multifractal method for geochemical and geophysical anomaly separation in Walsh domain , 2006, Comput. Geosci..

[3]  Weiji Wang,et al.  Purification and feature extraction of shaft orbits for diagnosing large rotating machinery , 2005 .

[4]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[5]  N. Ahmed,et al.  FAST TRANSFORMS, algorithms, analysis, applications , 1983, Proceedings of the IEEE.

[6]  Chen Zhen,et al.  Feature-Weighted K-Nearest Neighbor Algorithm with SVM , 2005 .

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

[8]  Sheng-Fa Yuan,et al.  Fault diagnostics based on particle swarm optimisation and support vector machines , 2007 .

[9]  Zhike Peng,et al.  Identification of the shaft orbit for rotating machines using wavelet modulus maxima , 2002 .

[10]  Jun S. Huang,et al.  Separating similar complex Chinese characters by Walsh transform , 1987, Pattern Recognit..

[11]  Sancho Salcedo-Sanz,et al.  Feature selection methods involving support vector machines for prediction of insolvency in non-life insurance companies , 2004, Intell. Syst. Account. Finance Manag..

[12]  Taro Shimogo,et al.  New indices in the sequency domain and their application to condition monitoring of mechanical systems , 1990 .