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Sensors 2018, 18(3), 704; https://doi.org/10.3390/s18030704

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings

1
School of Automobile and Transportation, Xihua University, Chengdu 610039, China
2
Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, China
3
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China
*
Authors to whom correspondence should be addressed.
Received: 17 January 2018 / Revised: 18 February 2018 / Accepted: 21 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions. View Full-Text
Keywords: steady-state index; threshold; railway axle bearing fault diagnosis; multiple bearing defects; the coefficient of variation; a shape function steady-state index; threshold; railway axle bearing fault diagnosis; multiple bearing defects; the coefficient of variation; a shape function
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yi, C.; Wang, D.; Fan, W.; Tsui, K.-L.; Lin, J. EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings. Sensors 2018, 18, 704.

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