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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.


Vibration signal of an axle bearing with a composite of three kinds of faults.
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sensors-15-10991-f014: Vibration signal of an axle bearing with a composite of three kinds of faults.

Mentions: From what has been discussed and analyzed above, using the IMF’s confidence index and Hilbert marginal spectrum to diagnose axle bearing faults is an advanced algorithm and signal processing technique. Since no hypothesis about the stationary and the periodicity of the signal must be respected for EEMD and marginal spectrum applications, the presented approach is tested on data collected for an axle bearing with a specific and single fault. The next question is whether the algorithms and signal processing by the IMF’s confidence index and Hilbert marginal spectrum can maintain their usefulness in a combined faults test. The vibration signal of an axle bearing with a composite of three kinds of faults: outer ring fault, cage fault and pin roller fault, as described as Figure 3a–c, is shown in Figure 14.


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)

Vibration signal of an axle bearing with a composite of three kinds of faults.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-10991-f014: Vibration signal of an axle bearing with a composite of three kinds of faults.
Mentions: From what has been discussed and analyzed above, using the IMF’s confidence index and Hilbert marginal spectrum to diagnose axle bearing faults is an advanced algorithm and signal processing technique. Since no hypothesis about the stationary and the periodicity of the signal must be respected for EEMD and marginal spectrum applications, the presented approach is tested on data collected for an axle bearing with a specific and single fault. The next question is whether the algorithms and signal processing by the IMF’s confidence index and Hilbert marginal spectrum can maintain their usefulness in a combined faults test. The vibration signal of an axle bearing with a composite of three kinds of faults: outer ring fault, cage fault and pin roller fault, as described as Figure 3a–c, is shown in Figure 14.

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.