期刊
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
卷 15, 期 4, 页码 1563-1572出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2017.2720177
关键词
Compound faults monitoring and diagnosis; discrete wavelet transform (DWT); generalized likelihood ratio test (GLRT); rolling bearing; statistical signal detection
资金
- Hong Kong Research Grants Council [11216014]
- National Natural Science Foundation of China [71402133, 71602155, 71572138, 11501209]
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University [sklms2016010]
This paper proposes a wavelet-based statistical signal detection approach for monitoring and diagnosis of bearing compound faults at an early stage. The bearing vibration signal is decomposed by an orthonormal discrete wavelet transform to obtain its energy dispersions at multiple levels. We investigate the statistical properties of the decomposed signal energy under both the normal and faulty conditions, based on which a generalized likelihood ratio test is developed. An exponentially weighted moving average control chart is then constructed to detect faults at an early stage. Simulation studies and a real case study are conducted to demonstrate the effectiveness of the proposed method. Furthermore, the comparison studies show that the proposed method outperforms the empirical mode decomposition method and Hilbert envelope spectrum analysis method.
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