Assessment of natural crack initiation and its propagation in slow speed bearings

Elforjani, Mohamed

doi:10.1080/10589750802339687

Cited by 20 publications

(17 citation statements)

References 13 publications

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“…Although conclusive, this research was not representative of broad operation condition as the test was at a slow rotational speed (72 r.p.m.). The results presented in this paper aim to compliment the work of Elforjani and Mba [7,8] by experimentally investigating the use of AE for detecting natural degradation of a bearing at a rotational speed of 1500 r.p.m. In addition, the use of the Kurtogram for improving signal-to-noise ratios on AE waveforms from a bearing is explored.…”

Section: Introductionsupporting

confidence: 52%

“…Additionally, Yoshioaka terminated the test once the AE level reached a certain predefined threshold; therefore propagation of the surface defect was not monitored. Later, Elforjani and Mba [7,8] conducted an experiment that was based on Yoshioka's work. Their results showed the effectiveness of AE in detecting the onset of bearing failure, identifying the circumferential location of the defect on the race at very early stages of degradation, and the diagnostic potential of the measured AE signal by enveloping and using the KS statistic.…”

Section: Introductionmentioning

confidence: 99%

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The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing

Eftekharnejad

Carrasco

Charnley

et al. 2011

Mechanical Systems and Signal Processing

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152

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a b s t r a c tThe application of Acoustic Emission (AE) technology for machine health monitoring is gaining ground as power tool for health diagnostic of rolling element bearing. This paper provides an investigation that compares the applicability of AE and vibration technologies in monitoring a naturally degraded roller bearing. This research is the first known attempt investigating the comparative effectiveness of applying the Kurtogram to both vibration and AE data from a defective bearing.

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Section: Introductionsupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing

Eftekharnejad

Carrasco

Charnley

et al. 2011

Mechanical Systems and Signal Processing

Self Cite

152

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“…They also indicated that the acoustic emission method can be used to detect the subsurface crack in the bearing. Elforjani and Mba [12] used a range of time and frequency domain analysis techniques to detect a…”

Section: Zmęczeniowe Pęknięcia Podpowierzchniowe Stanowiące Jedną Z mentioning

confidence: 99%

An investigation of a detection method for a subsurface crack in the outer race of a cylindrical roller bearing

Liu¹,

Shi²,

Shao³

2017

EiN

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An investigAtion of A detection method for A subsurfAce crAck in the outer rAce of A cylindricAl roller beAring bAdAniA metody wykrywAniA pęknięć podpowierzchniowych w zewnętrznej bieżni łożyskA wAlcowego tic emission and vibration methods to detect the propagation initiation and propagation time of rolling contact fatigue cracks. They denoted that the subsurface cracks can be detected by the acoustic emission method. Mano et al. [25] investigated the relationship between the growth of rolling contact fatigue cracks and load distribution on the inner race of a radial rolling bearing using a new acoustic emission source location method. They represented that the fatigue crack at the early stage would occur in a narrow area. Choudhury and Tandon [7] studied changes in the acoustic emission signal for defects with different sizes on the inner race and a roller of a cylindrical roller bearing. They denoted that the ringdown counts of the acoustic emission signal can be used to detect the defect in bearing raceways. Price et al. [27] and Schwach and Guo [31] also used the acoustic emission method to detect a subsurface crack caused by rolling contact fatigue. They also indicated that the acoustic emission method can be used to detect the subsurface crack in the bearing. Elforjani and Mba [12] used a range of time and frequency domain analysis techniques to detect a IntroductionRoller bearings are one of key components in various rotating machines. As one of major failure modes of the roller bearings due to periodic contact forces and external impulse loads, subsurface cracks in the bearings can significantly affect their dynamic performances [18,29], and produce catastrophic failures in these machines. Detecting the subsurface cracks in the races of the bearings at an early stage enables timely maintenance works to extend the lifetime of the machine system. One of current detection methods is vibration-based damage detection based on changes in dynamic characteristics [6,13]. Therefore, it is helpful to investigate the subsurface crack detection method for early failure detection and diagnosis for maintenance purposes.Many crack detection methods for rolling element bearings have been numerically and experimentally studied [21,22,29]. For instance, Yoshioka and Fujiwara [32] and Yoshioka [33] used the acousLiu J, Shi Z, ShAo Y. An investigation of a detection method for a subsurface crack in the outer race of a cylindrical roller bearing. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2017; 19 (2): 211-219, http://dx

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“…Ref. [1][2][3][4][5][6][7][8][9][10]. However, all these studies are conducted based on bearing seeded defect or through modification to the bearing components.…”

Section: Research Gapmentioning

confidence: 99%

“…its fault detection capability once the bearing degrades to crack propagation [1][2][3][4][5][6][7][8][9][10]. All experimental studies conducted so far, except those by Mba and his team at Cranfield University [ [1][2][3][4][5][6]53], have focused on seeded surface spalls and scratches, and despite suggesting a higher sensitivity of AE sensors, they have not investigated the possibility of detecting subsurface cracks. In the aforementioned studies at…”

Section: Acoustic Emissionmentioning

confidence: 99%

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

Kan¹

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Keywords AbstractBearings are one of the most commonly-used and crucial components in the majority of rotating machines in industrial applications. Failure of this component is common and can lead to machine breakdown. If it is not detected in time, bearing faults can even lead to catastrophic failures. An effective diagnostic system provides early warning and fault identification such that any machine breakdown can be avoided. Condition monitoring can benefit plant operators resulting in shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning.However, machines and associated systems today are increasingly complex, while diagnostic techniques have been mainly applied to relatively simple rotating machines. Therefore, there is a need for research on condition monitoring techniques that can be applied to complex systems in variable operating conditions. This thesis presents a study on bearing condition monitoring under variable operating condition using Support Vector Machines. Data collected from multiple sensors including accelerometers, acoustic emission sensors and tachometers are used for the studies presented in this thesis. Support Vector Machines are an efficient method for classification and prediction, and shows outstanding performance in modelling and generalization.The study begins with collecting bearing condition monitoring data. Vibration analysis is a well-established technique, while acoustic emission is gaining an increasing amount of attention especially on its capability to detect bearing incipient fault such as subsurface cracks. Existing studies so far have not demonstrated AE's diagnostic capabilities using naturally-damaged bearing, and only a very few studies adopt envelope and cyclostationary analysis on bearing fault diagnosis with this sensor technology. Due to the second-order cyclostationary nature of bearing signals and the success of envelope analysis in analysing bearing signals, the diagnostic and prognostic studies presented in this thesis make use of both envelope analysis and cyclostationary analysis to analyse vibration and acoustic emission bearing signals that will be collected from bearing diagnostics and run-to-failure prognostic experiments through a bearing diagnostic test-rig and a bearing prognostic test-rig.Using the collected data, the effectiveness and diagnostic capability of cyclostationary analysis will first be assessed using feature analysis with support vector machine. A diagnostic study is then conducted, investigating the diagnostic effectiveness of acoustic emission and its superiority to vibration, especially in the detection of bearing subsurface cracks. Through comparison of AE with vibration signals, this study confirms AE's superiority in detecting subsurface cracks in bearings and the absence of vibration symptoms of subsurface cracks.This diagnostic study has also shown the crucial ability of the cyclic logarithmic envelope iii spectrum in separating different cyc...

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Assessment of natural crack initiation and its propagation in slow speed bearings

Cited by 20 publications

References 13 publications

The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing

The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing

An investigation of a detection method for a subsurface crack in the outer race of a cylindrical roller bearing

Multi-Sensor Condition Monitoring of Bearings Using Support Vector Machines

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