Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 100, Issue -, Pages 743-765Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2017.08.002
Keywords
Rolling bearing; Feature learning; Improved convolutional deep belief network; Compressed sensing; Exponential moving average
Categories
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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The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods. (C) 2017 Elsevier Ltd. All rights reserved.
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