Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 75, Issue -, Pages 345-370Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2015.12.020
Keywords
Bearing; Fault diagnosis; Short-time matching; Support vector machine (SVM); Weak signal detection
Categories
Funding
- National Key Basic Research Program of China [2015CB057400]
- National Natural Science Foundation of China [51225501, 51335006]
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Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti noise ability and detect incipient fault, a novel fault detection method based on a short time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis. (C) 2016 Elsevier Ltd. All rights reserved.
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