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

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 (EEM...

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Published inSensors (Basel, Switzerland) Vol. 15; no. 5; pp. 10991 - 11011
Main Authors Yi, Cai, Lin, Jianhui, Zhang, Weihua, Ding, Jianming
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 11.05.2015
MDPI
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ISSN1424-8220
1424-8220
DOI10.3390/s150510991

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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.
AbstractList 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.
Author Ding, Jianming
Zhang, Weihua
Yi, Cai
Lin, Jianhui
AuthorAffiliation State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China; E-Mails: lin13008104673@126.com (J.L.); tpl@swjtu.edu.cn (W.Z.); fdingjianming@126.com (J.D.)
AuthorAffiliation_xml – name: State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China; E-Mails: lin13008104673@126.com (J.L.); tpl@swjtu.edu.cn (W.Z.); fdingjianming@126.com (J.D.)
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Issue 5
Keywords fault diagnostics
Hilbert transform
intrinsic mode function
marginal spectrum
axle bearing
ensemble empirical mode decomposition
Language English
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Snippet As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive...
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StartPage 10991
SubjectTerms Algorithms
axle bearing
Axles
Bearings
Confidence
Diagnostic software
ensemble empirical mode decomposition
Fault diagnosis
fault diagnostics
Hilbert transform
intrinsic mode function
marginal spectrum
Railroads
Railway engineering
Railways
Sensors
Signal processing
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Title Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD
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