Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine
Joint Authors
Zhang, Xiaochen
Jiang, D. X.
Wang, N. F.
Han, Te
Yang, Wenguang
Yang, Yizhou
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-22
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed.
Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set.
Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set.
Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set.
After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained.
Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model.
The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.
American Psychological Association (APA)
Zhang, Xiaochen& Jiang, D. X.& Han, Te& Wang, N. F.& Yang, Wenguang& Yang, Yizhou. 2017. Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine. Journal of Sensors،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1187437
Modern Language Association (MLA)
Zhang, Xiaochen…[et al.]. Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine. Journal of Sensors No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1187437
American Medical Association (AMA)
Zhang, Xiaochen& Jiang, D. X.& Han, Te& Wang, N. F.& Yang, Wenguang& Yang, Yizhou. Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1187437
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187437