Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 65, Issue 3, Pages 2727-2736Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2017.2745473
Keywords
Convolutional deep belief network (CDBN); electric locomotive bearing; exponential moving average (EMA); fault diagnosis; feature learning
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Funding
- National Natural Science Foundation of China [51475368]
- Shanghai Engineering Research Center of Civil Aircraft Health Monitoring Foundation of China [GCZX-2015-02]
- Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201710]
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Bearing fault diagnosis is of significance to enhance the reliability and security of electric locomotive. In this paper, a novel convolutional deep belief network (CDBN) is proposed for bearing fault diagnosis. First, an auto-encoder is used to compress data and reduce the dimension. Second, a novel CDBN is constructed with Gaussian visible units to learn the representative features. Third, exponential moving average is employed to improve the performance of the constructed deep model. The proposed method is applied to analyze experimental signals collected from electric locomotive bearings. The results show that the proposed method is more effective than the traditional methods and standard deep learning methods.
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