Bearing defect detection and diagnosis using a time encoded signal processing and pattern recognition method

Abdusslam, S.A.; Raharjo, P.; Gu, Fengshou; Ball, Andrew

doi:10.1088/1742-6596/364/1/012036

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…A range of factors can contribute to premature bearing failure, including lubrication failure, excessive temperature and improper installation. 1,2 Approaches to the fault detection and diagnosis of bearings exist based on a variety of measured parameters, including lubricant wear debris, 3 temperature, 4 acoustic emission, 5,6 airborne acoustics 7 and vibration. 8 Of these different methods, vibration-based approaches remain the most extensively employed in condition monitoring of bearings, and they have been proven to be both efficient and effective in the detection, diagnosis and location of defects in rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

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A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

et al. 2020

Structural Health Monitoring

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Bearings are used as the most important load-carrying transmission components in various machines, thus subjecting to a number of faults including wear, fatigue pitting, cracks and so on. Fault detection and diagnosis of bearings can effectively prevent the machine from such typical failures and subsequent consequences. The faults in bearings can lead to the vibration signals that exhibit cyclostationary characteristics due to the inevitably random phase noise (or slippage between bearing components). In this article, a phase linearisation–based modulation signal bispectrum is proposed to tune up the cyclostationary bearing signal into a periodic waveform by linearizing the instantaneous phase of the narrow frequency band signals. In this way, the signal becomes more deterministic and modulation signal bispectrum can be effectively applied to suppression noise and obtain accurate and robust diagnosis results. As a result, this fault detector can achieve high performance in characterising the nonstationary bearing vibration signals and hence diagnose the bearing faults even in the case of extremely low signal-to-noise ratio (<−20 dB), which is benchmarked by the method of conventional modulation signal bispectrum in both simulation and experiment studies.

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Section: Introductionmentioning

confidence: 99%

“…8 Of these different methods, vibration-based approaches remain the most extensively employed in condition monitoring of bearings, and they have been proven to be both efficient and effective in the detection, diagnosis and location of defects in rolling bearings. 1…”

Section: Introductionmentioning

confidence: 99%

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

et al. 2020

Structural Health Monitoring

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show abstract

“…By dividing the continuous data state space into amounts of discrete cell and gives each cell a number symbol, which transforms the complex data into symbols sequence flow. The signal characteristics of large scale data are able to be captured for reducing the dynamics noise impact in this method [11,12].…”

Section: Tesparmentioning

confidence: 99%

Assessment Method for Rolling Bearing Performance Degradation Using TESPAR and GMM

Zhang¹,

Huang²,

Xiong³

et al. 2014

TOMEJ

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Rolling bearing performance degradation assessment has been receiving much attention for which itscrucial role to realize CBM(condition-based maintenance).This paper proposed a novel bearing performance degradation method based on TESPAR(Time Encoded Signal Processing and Recognition)and GMM(Gauss Mixture Model). TESPAR is used to extracted features which constitute A-matrix. GMM is utilized to approximate the density distribution of singular values decomposed by A-matrix. TENLLP(Time-Encoded Negative Log Likelihood Probability) serves as a fault severity which can display the similarity of the singular values between normal samples and fault samples as quantificational. Results of its application to bearing fatigue test show that this performance degradation assessment can detect the incipient rolling bearing fault and be sensitive to the change of fault.

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Automatic detection of bearing faults

Bhonde¹,

Panchwadkar²

2023

Materials Today: Proceedings

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Bearing defect detection and diagnosis using a time encoded signal processing and pattern recognition method

Cited by 5 publications

References 6 publications

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

Assessment Method for Rolling Bearing Performance Degradation Using TESPAR and GMM

Automatic detection of bearing faults

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