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Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm
Joint Authors
Pan, Shuang
Han, Tian
Tan, Andy C. C.
Lin, Tian Ran
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-11
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine.
Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals.
Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales.
The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults.
The first five most important features are selected from the feature vectors and classified using support vector machine.
The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig.
The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy.
It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.
American Psychological Association (APA)
Pan, Shuang& Han, Tian& Tan, Andy C. C.& Lin, Tian Ran. 2016. Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm. Shock and Vibration،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1119363
Modern Language Association (MLA)
Pan, Shuang…[et al.]. Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm. Shock and Vibration No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1119363
American Medical Association (AMA)
Pan, Shuang& Han, Tian& Tan, Andy C. C.& Lin, Tian Ran. Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1119363
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119363