期刊
JOURNAL OF SOUND AND VIBRATION
卷 439, 期 -, 页码 271-286出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jsv.2018.09.054
关键词
Planetary gearbox; Vold-Kalman filter; Laplacian score; Fault pattern identification
资金
- China Postdoctoral Innovative Talent Pland, China [W016342]
- National Natural Science Foundation of China, China [51805434, 51375078]
- Natural Science and Engineering Research Council of Canada, Canada [RGPIN-2015-04897]
- University of Manitoba Research Start-up Funds, Canada
This paper presents a novel signal processing scheme by combining an improved Vold-Kalman filter and the multi-scale sample entropy (IVKF-MSSE) for planetary gearboxes under non-stationary working conditions. In this scheme, we propose a method based on the characteristic frequency ratio (CFR) to select the VKF bandwidth. First, a CFR is adopted to select a VKF bandwidth with the largest CFR value as the optimal VKF bandwidth. Second, IVKF is used to extract fault-induced information under time-varying speed conditions. Because an optimal bandwidth is used in VKF, the feature extraction capability of VKF is enhanced. Then, the MSSE is applied to extract gearbox fault features. After that, the Laplacian score (LS) approach is introduced to refine the fault features by sorting the scale factors. At the end, the selected features are fed into the least square support vector machine (LSSVM) for effective fault pattern identification. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the auto-regressive AR-MSSE, VKF-MSSE and EEMD-MSSE in identifying fault types of planetary gearboxes. (C) 2018 Elsevier Ltd. All rights reserved.
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