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Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine,10.1016/j.eswa.2008.09.033,Expert Systems With Appli

Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine   (Citations: 12)
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This study concerns with fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various types of bearing defects associated with shaft speeds as low as 10rpm under several loading conditions. The data was acquired from the low speed bearing test rig using acoustic emission (AE) and accelerometer sensors under a constant load with different speeds. The aim of this study is to address the problem of detecting an incipient bearing fault and to find reliable methods for low speed machine fault diagnosis. In this paper, two methods of multi-class classification techniques for fault diagnosis through RVM and SVM are presented and the effectiveness of using AE and vibration signals due to low impact rate for low speed diagnosis. In the present study, component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. The classification for fault diagnosis was also conducted using original data feature and without feature extraction. The result shows that multi-class RVM produces promising results and has the potential for use in fault diagnosis of low speed machine.
Journal: Expert Systems With Applications - ESWA , vol. 36, no. 3, pp. 7252-7261, 2009
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    • ..., kernel PCA on support vector machines(KPCA-SVM) [4] , kernel FDA �ƒ KFDA�≈ [5] , kernel ICA on support vector machines(KICA-SVM) [6] , kernel ICA on FDA(KICA-FDA) [7...

    Jun Yiet al. Nonlinear feature selection based on hybrid KCCA-FNN algorithm for mod...

    • ...Some methods of feature extraction and subspace modeling based on kernel function had presented to reduce the original features such as KPCR(kernel PCR)[2],KPCA-NN[3],KPCA-SVM[4],KFDA[5],KICA-SVM[6] and KICA-FDA[7,8]...

    Jun Yiet al. A variable selection method based on KPCA and FNN for nonlinear system...

    • ...Finally, the choice of the classification system ranges in the literature from the use of neural networks (NNs), such as Multi-Layer Perceptron NNs (MLPs) and Radial Basis Function NNs, to the use of support vector machines and relevance vector machines, like in [16], [17], and in [1]...

    Sara Lioba Volpiet al. Rolling element bearing diagnosis using convex hull

    • ...Recently, various intelligent classification algorithms have been successfully applied to automatic detection and diagnosis of machine conditions, such as artificial neural networks (ANN) [5-8], support vector machines (SVM) [3, 4, 8-10] and relevance vector machines (RVM) [10-12] and so on. RVM proposed by Tipping in 2000 [13, 14] is a Bayesian machine learning technique for regression and classification...
    • ...Recently, various intelligent classification algorithms have been successfully applied to automatic detection and diagnosis of machine conditions, such as artificial neural networks (ANN) [5-8], support vector machines (SVM) [3, 4, 8-10] and relevance vector machines (RVM) [10-12] and so on. RVM proposed by Tipping in 2000 [13, 14] is a Bayesian machine learning technique for regression and classification...
    • ...These remarkable properties make RVM more suitable than SVM for online application [10-12]...

    Chuangxin Heet al. Intelligent gear fault detection based on relevance vector machine wit...

    • ...For instance, Widodo [13] applied time-domain statistical features for detection of defect on low speed bearing, such as mean, standard deviation, skewness and kurtosis...

    Jianhua Zhonget al. Machine condition monitoring and fault diagnosis based on support vect...

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