Journal
MEASUREMENT
Volume 82, Issue -, Pages 439-449Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2016.01.023
Keywords
Wheel-bearing; Vibration signal; Empirical wavelet transform; Faults diagnosis
Funding
- National Natural Science Foundation of China [51421004, 51575423]
- Science and Technology Research and Development Project in Shaanxi Province of China [2014KJXX-33]
- Fundamental Research Funds for the Central University
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Rolling bearings are used widely as wheel bearing in trains. Fault detection of the wheelbearing is of great significance to maintain the safety and comfort of train. Vibration signal analysis is the most popular technique that is used for rolling element bearing monitoring, however, the application of vibration signal analysis for wheel bearings is quite limited in practice. In this paper, a novel method called empirical wavelet transform (EWT) is used for the vibration signal analysis and fault diagnosis of wheel-bearing. The EWT method combines the classic wavelet with the empirical mode decomposition, which is suitable for the non-stationary vibration signals. The effectiveness of the method is validated using both simulated signals and the real wheel-bearing vibration signals. The results show that the EWT provides a good performance in the detection of outer race fault, roller fault, and the compound fault of outer race and roller. (C) 2016 Elsevier Ltd. All rights reserved.
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