期刊
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
卷 99, 期 5-8, 页码 1195-1201出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00170-018-2553-1
关键词
Ball bearing; Fault diagnosis; Sparse matrices; Wavelet transforms
资金
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM113242, R01GM122080] Funding Source: NIH RePORTER
- NIGMS NIH HHS [R01 GM113242, R01 GM122080] Funding Source: Medline
The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal is filtered using the EWT. The l(q)-regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.
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