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Sensors 2015, 15(5), 10991-11011; doi:10.3390/s150510991

Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD

State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China
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Academic Editor: Vittorio M.N. Passaro
Received: 27 March 2015 / Revised: 4 May 2015 / Accepted: 4 May 2015 / Published: 11 May 2015
(This article belongs to the Section Physical Sensors)
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Abstract

As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure based on ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings. EEMD overcomes the limitations that often hypothesize about data and computational efforts that restrict the application of signal processing techniques. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert instantaneous frequency spectrum and marginal spectrum. Anyhow, not all the IMFs obtained by the decomposition should be considered into Hilbert marginal spectrum. The IMFs’ confidence index arithmetic proposed in this paper is fully autonomous, overcoming the major limit of selection by user with experience, and allows the development of on-line tools. The effectiveness of the improvement is proven by the successful diagnosis of an axle bearing with a single fault or multiple composite faults, e.g., outer ring fault, cage fault and pin roller fault. View Full-Text
Keywords: ensemble empirical mode decomposition; Hilbert transform; axle bearing; fault diagnostics; intrinsic mode function; marginal spectrum ensemble empirical mode decomposition; Hilbert transform; axle bearing; fault diagnostics; intrinsic mode function; marginal spectrum
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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MDPI and ACS Style

Yi, C.; Lin, J.; Zhang, W.; Ding, J. Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD. Sensors 2015, 15, 10991-11011.

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