A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery
Joint Authors
Wei, Kexiang
Luo, Songrong
Cheng, Junsheng
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-20
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Luo, Songrong& Cheng, Junsheng& Wei, Kexiang. 2016. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery. Shock and Vibration،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1119271
Modern Language Association (MLA)
Luo, Songrong…[et al.]. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery. Shock and Vibration No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1119271
American Medical Association (AMA)
Luo, Songrong& Cheng, Junsheng& Wei, Kexiang. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1119271
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119271