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Shock and Vibration
Volume 2016, Article ID 5141564, 10 pages
Research Article

A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

1Cooperative Innovation Center for the Construction and Development of Dongting Lake Ecological Economic Zone, Changde 415000, China
2College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415000, China
3College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
4Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Hunan Institute of Engineering, Xiangtan 411101, China

Received 12 March 2016; Revised 4 June 2016; Accepted 12 July 2016

Academic Editor: Lorenzo Dozio

Copyright © 2016 Songrong Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD) can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD) technique based local characteristic-scale decomposition (LCD) was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN) and mean impact value (MIV), ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.