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| Fault Diagnosis Model Based on Dimension Reduction with Linear Local Tangent Space Alignment |
LI Feng TANG Bao-ping CHEN Fa-fa
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| The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China |
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Abstract Abstract：Based on dimension reduction with Linear Local Tangent Space Alignment (LLTSA), a novel fault diagnosis model is proposed in this paper to achieve automation、high-precision and generality of fault diagnosis of rotating machinery. With this model, mixed-domain feature sets of training and test samples are first constructed to characterize the property of each fault comprehensively by the fusion of Empirical Mode Decomposition (EMD) and Autoregression(AR) model coefficients. After that, LLTSA is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination. Finally, the low-dimensional eigenvectors of training and test samples are input into K-nearest neighbors classifier (KNNC) to carry out fault diagnosis. Compared to the existing approaches, the proposed diagnosis model combines the strengths of mixed-domain features fusion in extensive extraction of fault feature, LLTSA in effective compression of fault information and KNNC in classification decision-making, and realizes the automation、high-precision and generality of fault diagnosis method. The diagnosis example on different fault positions and severities of deep groove ball bearings validates the effectivity of proposed fault diagnosis model.
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Received: 02 April 2011
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2012, Vol. 31 
