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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.


Hilbert marginal spectrum of selected IMFs for axle bearing vibrations with outer ring faults.
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sensors-15-10991-f011: Hilbert marginal spectrum of selected IMFs for axle bearing vibrations with outer ring faults.

Mentions: The Hilbert marginal spectra of axle bearing vibration signals for four working conditions with different faults are shown in Figure 10, Figure 11, Figure 12 and Figure 13, respectively. Figure 10 shows the Hilbert marginal spectrum of selected IMFs for undamaged axle bearing vibration signal. There are multiple frequency peaks, and the main frequency band cannot be found. This can be explained by the fact that the energy distribution is uniform in the frequency direction when there is nothing damaged on the axle bearing.


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)

Hilbert marginal spectrum of selected IMFs for axle bearing vibrations with outer ring faults.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-10991-f011: Hilbert marginal spectrum of selected IMFs for axle bearing vibrations with outer ring faults.
Mentions: The Hilbert marginal spectra of axle bearing vibration signals for four working conditions with different faults are shown in Figure 10, Figure 11, Figure 12 and Figure 13, respectively. Figure 10 shows the Hilbert marginal spectrum of selected IMFs for undamaged axle bearing vibration signal. There are multiple frequency peaks, and the main frequency band cannot be found. This can be explained by the fact that the energy distribution is uniform in the frequency direction when there is nothing damaged on the axle bearing.

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.