Fault Detection of Rolling Element Bearings using Advanced Signal Processing Technique

Azeez, Abid Abdul; Alkhedher, Mohammad; Gadala, Mohamed S.; Mohamad, Omar Ahmad

doi:10.1109/aset48392.2020.9118398

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…51 With further increasing speed, again cyclostationary techniques are explored for localised defects, [52][53][54] with studies reporting improved early detection through the application of short-time techniques, 55,56 spectral correlation 57 and spectral kurtosis, 58 and studies reporting improved performance in a high-noise environment by applying self-adaptive noise cancellation, 59 least mean squares filtering [60][61][62] or wavelet-based filtering. [63][64][65] Additionally, machine learning techniques, such as neural networks, have been suggested and implemented to identify characteristic defect frequencies from spectrograms. 66,67 Alternative to cyclostationary techniques, time-of-arrival-based localisation is demonstrated for the detection of localised defects in static raceways, 68 and the fusion of AE and vibrationbased multi-feature entropy distance is proposed for damaged-component identification.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Scheeren

Kaminski

Pahlavan

2023

Structural Health Monitoring

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This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.

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Section: Introductionmentioning

confidence: 99%

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Scheeren

Kaminski

Pahlavan

2023

Structural Health Monitoring

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“…Additionally, careful claims are made on the potential of AE to serve as an early warning system, based on the probable detection of early stage subsurface cracking in run-to-failure experiments [25], while also mitigating the uncertainties related to varying operational conditions through probabilistic modelling [26]. Broadly formulated, the state of the art focuses on improved robustness for damage detection [27][28][29], and the estimation of both damage severity [30][31][32][33] and location [34]. For the latter, machine learning techniques are also employed, such as discrete hidden Markov models [35], neural networks [36], and deep learning [37].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring

Scheeren

Kaminski

Pahlavan

2022

Sensors

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In the condition monitoring of bearings using acoustic emission (AE), the restriction to solely instrument one of the two rings is generally considered a limitation for detecting signals originating from defects on the opposing non-instrumented ring or its interface with the rollers due to the signal energy loss. This paper presents an approach to evaluate transmission in low-speed roller bearings for application in passive ultrasound monitoring. An analytical framework to describe the propagation and transmission of ultrasonic waves through the geometry and interfaces of a bearing is presented. This framework has been used to evaluate the transmission of simulated damage signals in an experiment with a static bearing. The results suggest that low- to mid-frequency signals (<200 kHz), when passing through the rollers and their interfaces from one raceway to the other, can retain enough energy to be potentially detected. An average transmission loss in the range of 10–15 dB per interface was experimentally observed.

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Fault Detection of Rolling Element Bearings using Advanced Signal Processing Technique

Cited by 2 publications

References 7 publications

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring

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