期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 72-73, 期 -, 页码 160-183出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2015.10.017
关键词
Mechanical fault diagnosis; Feature extraction; Empirical wavelet transform; Data-driven adaptive Fourier spectrum segment
资金
- National Natural Science Foundation of China for Innovation Research Group [51421004]
- National Natural Science Foundation of China [51405379]
- China Postdoctoral Science Foundation [2014M562396, 2015T81017]
- Fundamental Research Funds for the Central Universities of China [XJJ2015106, CXTD2014001]
- Shaanxi Industrial Science and Technology Project [2015GY121]
Due to the multi-modulation feature in most of the vibration signals, the extraction of embedded fault information from condition monitoring data for mechanical fault diagnosis still is not a relaxed task. Despite the reported achievements, Wavelet transform follows the dyadic partition scheme and would not allow a data-driven frequency partition. And then Empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis and non-dyadic partition scheme. However, the pre-defined segment way of Fourier spectrum without dependence on analyzed signals may result in inaccurate mono component identification. In this paper, the modified EWT (MEWT) method via data driven adaptive Fourier spectrum segment is proposed for mechanical fault identification. First, inner product is calculated between the Fourier spectrum of analyzed signal and Gaussian function for scale representation. Then, adaptive spectrum segment is achieved by detecting local minima of the scale representation. Finally, empirical modes can be obtained by adaptively merging mono-components based on their envelope spectrum similarity. The adaptively extracted empirical modes are analyzed for mechanical fault identification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance. (C) 2015 Elsevier Ltd. All rights reserved.
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