The Use of Acoustic Emission for Estimation of Bearing Defect Size

Mba, David

doi:10.1007/s11668-008-9119-8

Cited by 41 publications

(24 citation statements)

References 9 publications

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“…Despite of the success of vibration-based methodologies, over the last decades bearing condition monitoring techniques based on Acoustic Emission (AE) have become very popular [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. AE has demonstrated to be a very powerful tool for fault detection and diagnosis particularly in bearings, and some recent studies [13; 32; 33] reported that AE can be more sensitive in detecting incipient faults in bearings over other methodologies based on vibration data.…”

Section: Condition Monitoring Of Bearings Based On Acoustic Emissionmentioning

confidence: 99%

Use of Spectral Kurtosis for Improving Signal to Noise Ratio of Acoustic Emission Signal from Defective Bearings

Ruiz-Cárcel

Hernani-Ros

Cao

et al. 2014

J Fail. Anal. and Preven.

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The use of Acoustic Emission (AE) to monitor the condition of roller bearings in rotating machinery is growing in popularity. This investigation is centred on the application of Spectral Kurtosis (SK) as a denoising tool able to enhance the bearing fault features from an AE signal. This methodology was applied to AE signals acquired from an experimental investigation where different size defects were seeded on a roller bearing. The results suggest that the signal to noise ratio can be significantly improved using SK.

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Section: Condition Monitoring Of Bearings Based On Acoustic Emissionmentioning

confidence: 99%

Use of Spectral Kurtosis for Improving Signal to Noise Ratio of Acoustic Emission Signal from Defective Bearings

Ruiz-Cárcel

Hernani-Ros

Cao

et al. 2014

J Fail. Anal. and Preven.

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“…The high sensitivity of AE technique and AE parameters in detecting the incipient bearing faults has become one of the significant advantages of AE over vibration measurement. 3 A comprehensive review of AE application for bearing fault detection was presented by Mba et al 4 Al-Ghamd et al 5,6 investigated the relationship between AE parameters in time domain and defect size. They concluded that AE burst duration is an effective parameter for identifying defect size on the outer race.…”

Section: Introductionmentioning

confidence: 99%

“…4 Al-Ghamd et al. 5,6 investigated the relationship between AE parameters in time domain and defect size. They concluded that AE burst duration is an effective parameter for identifying defect size on the outer race.…”

Section: Introductionmentioning

confidence: 99%

Optimized statistical parameters of acoustic emission signals for monitoring of rolling element bearings

Hemmati

Alqaradawi

Gadala

2015

Proceedings of the Institution of Mechanical Engineers, Part J:

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Acoustic emission (AE) signal generated from artificial defects in rolling element bearings are investigated using experimental measurements in this paper. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, faults diagnostics, defect size calculation, and prognostics. In this paper, the effect of defect size, operating speed, and loading conditions on statistical parameters of AE signals, using design of experiment method, have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings.

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“…[13][14][15] AE techniques have numerous advantages over vibrationbased approaches. Namely, AE techniques are capable of incipient fault detection.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on a new acoustic emission sensor technique

Hecke

Qu²,

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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The diagnosis of bearing health by quantifying acoustic emission data has been an area of interest for recent years due to the numerous advantages over vibration-based techniques. However, most acoustic emission-based methodologies to date are data-driven technologies. This research takes a novel approach combining a heterodyne-based frequency reduction technique, time synchronous resampling, and spectral averaging to process acoustic emission signals and extract condition indicators for bearing fault diagnosis. The heterodyne technique allows the acoustic emission signal frequency to be shifted from several megahertz to less than 50 kHz, which is comparable to that of vibration-based techniques. Then, the digitized signal is band-pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the acoustic emission data, allowing the computation of a spectral average which in turn enables the extraction and evaluation of condition indicators for bearing fault diagnosis. The presented technique is validated using the acoustic emission signals of seeded fault steel bearings on a bearing test rig. The result is an effective acoustic emission-based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage.

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The Use of Acoustic Emission for Estimation of Bearing Defect Size

Cited by 41 publications

References 9 publications

Use of Spectral Kurtosis for Improving Signal to Noise Ratio of Acoustic Emission Signal from Defective Bearings

Use of Spectral Kurtosis for Improving Signal to Noise Ratio of Acoustic Emission Signal from Defective Bearings

Optimized statistical parameters of acoustic emission signals for monitoring of rolling element bearings

Bearing fault diagnosis based on a new acoustic emission sensor technique

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