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

Yi C, Lin J, Zhang W, Ding J - Sensors (Basel) (2015)

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China. Justin.yi@163.com.

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.

No MeSH data available.


IMFs for the axle bearing vibration signal with cage fault.
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sensors-15-10991-f007: IMFs for the axle bearing vibration signal with cage fault.

Mentions: To discriminate the four conditions, the EEMD is applied to all of these original signals: there are 12 IMFs decomposed in total for the four vibration signals, and the twelfth IMF is the residue on behalf of the trend. All the IMFs extracted are reported in Figure 5, Figure 6. Figure 7 and Figure 8, respectively.


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

Yi C, Lin J, Zhang W, Ding J - Sensors (Basel) (2015)

IMFs for the axle bearing vibration signal with cage fault.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481964&req=5

sensors-15-10991-f007: IMFs for the axle bearing vibration signal with cage fault.
Mentions: To discriminate the four conditions, the EEMD is applied to all of these original signals: there are 12 IMFs decomposed in total for the four vibration signals, and the twelfth IMF is the residue on behalf of the trend. All the IMFs extracted are reported in Figure 5, Figure 6. Figure 7 and Figure 8, respectively.

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China. Justin.yi@163.com.

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.

No MeSH data available.