Bearing Fault Evaluation for Structural Health Monitoring, Fault Detection, Failure Prevention and Prognosis

Saxena, Madhavendra; Bannett, Olvin Oliver; Sharma, Vivek Kumar

doi:10.1016/j.proeng.2016.05.026

Cited by 14 publications

(3 citation statements)

References 7 publications

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“…The envelope is achieved by FFT to get "Fault Order Spectrum" for each IMF. Finally, different bearing fault types (outer race, inner race, rolling element) can be judged through "Fault Order Spectrum" [2] .…”

Section: Kwp Algorithmmentioning

confidence: 99%

“…The main fundamental and fault characteristic frequencies are explained in [1] . As internal components of a bearing have impacts during rotation under most of faults, vibration and acoustic emission analysis are well-known techniques for condition monitoring of rolling bearings [1,2] . However, these techniques usually require each bearing to be fitted directly with on-board condition monitoring equipment, and this is an expensive solution, especially for systems with a large number of mobile assets, such as a railway network.…”

Section: Introductionmentioning

confidence: 99%

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Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

Chen¹,

Gao²,

Peng³

et al. 2018

AIP Conference Proceedings

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Abstract. As one of the key components of railway vehicles, the operation condition of the axle box bearing has a significant effect on traffic safety. The acoustic diagnosis is more suitable than vibration diagnosis for trackside monitoring. The acoustic signal generated by the train axle box bearing is an amplitude modulation and frequency modulation signal with complex train running noise. Although empirical mode decomposition (EMD) and some improved time-frequency algorithms have proved to be useful in bearing vibration signal processing, it is hard to extract the bearing fault signal from serious trackside acoustic background noises by using those algorithms. Therefore, a kurtosis-optimization-based wavelet packet (KWP) denoising algorithm is proposed, as the kurtosis is the key indicator of bearing fault signal in time domain. Firstly, the geometry based Doppler correction is applied to signals of each sensor, and with the signal superposition of multiple sensors, random noises and impulse noises, which are the interference of the kurtosis indicator, are suppressed. Then, the KWP is conducted. At last, the EMD and Hilbert transform is applied to extract the fault feature. Experiment results indicate that the proposed method consisting of KWP and EMD is superior to the EMD.

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Section: Kwp Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

Chen¹,

Gao²,

Peng³

et al. 2018

AIP Conference Proceedings

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show abstract

“…Ceyhan et al discussed the wheel hub fatigue performance under non-constant rotational loading [4]. Further, the bearing fault evaluation is carried out for structural health monitoring, fault detection, failure prevention, and prognosis [5]. The measurements methods, advantages, and disadvantages of fault diagnosis methods were discussed for bearing health monitoring through shaft signals [6].…”

Section: Introductionmentioning

confidence: 99%

A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings

Tang

et al. 2019

Applied Sciences

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The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.

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Investigation of Predictive Maintenance Algorithms for Rotating Shafts Under Various Bending Loads

Basson

Bright

Padayachee

et al. 2023

Lecture Notes in Production Engineering

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Bearing Fault Evaluation for Structural Health Monitoring, Fault Detection, Failure Prevention and Prognosis

Cited by 14 publications

References 7 publications

Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings

Investigation of Predictive Maintenance Algorithms for Rotating Shafts Under Various Bending Loads

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