Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair
Joint Authors
Lu, Yixiang
Ma, Dan
Zhang, Yushun
Bao, Hua
Peng, Xueming
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-22
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue.
Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper.
The proposed algorithm mainly consists of three components.
First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra.
This similarity ensures that we can collaboratively represent any test sample by using training samples.
Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal.
Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification.
Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings.
The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals.
The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.
American Psychological Association (APA)
Ma, Dan& Lu, Yixiang& Zhang, Yushun& Bao, Hua& Peng, Xueming. 2019. Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair. Shock and Vibration،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1211188
Modern Language Association (MLA)
Ma, Dan…[et al.]. Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair. Shock and Vibration No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1211188
American Medical Association (AMA)
Ma, Dan& Lu, Yixiang& Zhang, Yushun& Bao, Hua& Peng, Xueming. Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1211188
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1211188