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 in | Sensors (Basel, Switzerland) Vol. 15; no. 5; pp. 10991 - 11011 | 
|---|---|
| Main Authors | , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Switzerland
          MDPI AG
    
        11.05.2015
     MDPI  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1424-8220 1424-8220  | 
| DOI | 10.3390/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. | 
    
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| 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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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25970256$$D View this record in MEDLINE/PubMed | 
    
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| Keywords | fault diagnostics Hilbert transform intrinsic mode function marginal spectrum axle bearing ensemble empirical mode decomposition  | 
    
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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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