Limits...
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 all IMFs for axle bearing vibration: (a) undamaged bearing; (b) outer ring fault bearing; (c) cage fault bearing; (d) pin roller fault bearing.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4481964&req=5

sensors-15-10991-f009: Hilbert marginal spectrum of all IMFs for axle bearing vibration: (a) undamaged bearing; (b) outer ring fault bearing; (c) cage fault bearing; (d) pin roller fault bearing.

Mentions: The Hilbert marginal spectrums of all IMFs of axle bearing vibration signals under four working conditions are calculated for the comparison with the minority IMFs selected by the proposed method, showed as Figure 9a–d, respectively. The Hilbert marginal spectrum represents the fluctuation of the energy distribution of axle bearing vibration with frequency, and the larger the amplitude, the more the energy distribution of the frequency band is. There is a main frequency peak approximating 51 Hz, which is consistent in each marginal spectrum in Figure 9, and corresponds to the five multiplier rotation frequency of the axle bearing at the wheel speed of 100 km/h.


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 all IMFs for axle bearing vibration: (a) undamaged bearing; (b) outer ring fault bearing; (c) cage fault bearing; (d) pin roller fault bearing.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-10991-f009: Hilbert marginal spectrum of all IMFs for axle bearing vibration: (a) undamaged bearing; (b) outer ring fault bearing; (c) cage fault bearing; (d) pin roller fault bearing.
Mentions: The Hilbert marginal spectrums of all IMFs of axle bearing vibration signals under four working conditions are calculated for the comparison with the minority IMFs selected by the proposed method, showed as Figure 9a–d, respectively. The Hilbert marginal spectrum represents the fluctuation of the energy distribution of axle bearing vibration with frequency, and the larger the amplitude, the more the energy distribution of the frequency band is. There is a main frequency peak approximating 51 Hz, which is consistent in each marginal spectrum in Figure 9, and corresponds to the five multiplier rotation frequency of the axle bearing at the wheel speed of 100 km/h.

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.