Browsing by Author "Elforjani, Mohamed"

Now showing 1 - 4 of 4
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    Assessment of natural crack initiation and its propagation in slow speed bearings
    (Taylor & Francis, 2009-09-30T00:00:00Z) Elforjani, Mohamed; Mba, David
    Monitoring of bearings is an essential part of most condition monitoring programmes in rotating machinery. This paper demonstrates the use of acoustic emission (AE) measurements to detect, monitor and locate natural defect initiation and propagation in a thrust rolling element bearing. To undertake this task a special purpose test-rig was built that allowed for accelerated natural degradation of a bearing race. It is concluded that sub-surface initiation and subsequent crack propagation can be detected using a range of time and frequency domain analysis techniques on AE's generated from natural degrading bearings. The paper also investigates the source characterisation of AE signals associated with a defective bearing whilst in operation.
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    Detecting natural crack initiation and growth in slow speed shafts with the Acoustic Emission technology
    (Elsevier Science B.V., Amsterdam., 2009-10-01T00:00:00Z) Elforjani, Mohamed; Mba, David
    This paper presents results of an experimental investigation to assess the potential of the Acoustic Emission (AE) technology for detecting natural cracks in operational slow speed shafts. A special purpose built test rig was employed for generating natural degradation on a shaft. It was concluded that AE technology successfully detected natural cracks induced on slow speed shafts.
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    Detection of natural crack in wind turbine gearbox
    (Elsevier, 2017-10-30) Shanbr, Suliman; Elasha, Faris; Elforjani, Mohamed; Teixeira, Joao Amaral
    One of the most challenging scenarios in bearing diagnosis is the extraction of fault signatures from within other strong components which mask the vibration signal. Usually, the bearing vibration signals are dominated by those of other components such as gears and shafts. A good example of this scenario is the wind turbine gearbox which presents one of the most difficult bearing detection tasks. The non-stationary signal analysis is considered one of the main topics in the field of machinery fault diagnosis. In this paper, a set of signal processing techniques has been studied to investigate their feasibility for bearing fault detection in wind turbine gearbox. These techniques include statistical condition indicators, spectral kurtosis, and envelope analysis. The results of vibration analysis showed the possibility of bearing fault detection in wind turbine high-speed shafts using multiple signal processing techniques. However, among these signal processing techniques, spectral kurtosis followed by envelope analysis provides early fault detection compared to the other techniques employed. In addition, outer race bearing fault indicator provides clear indication of the crack severity and progress.
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    ItemOpen Access
    Natural mechanical degradation measurements in slow speed bearings
    (Elsevier Science B.V., Amsterdam., 2009-01-31T00:00:00Z) Elforjani, Mohamed; Mba, David
    Acoustic emission (AE) technology applied to condition monitoring is gaining acceptance as a useful complimentary tool. This paper demonstrates the use of AE measurements to detect, monitor the growth and locate natural defect initiation and propagation in a conventional rolling element bearing. To undertake this task a special purpose test-rig was built to allow for accelerated natural degradation of a bearing race. It is concluded that crack initiation and its subsequent propagation is detectable with AE technology. The paper also investigates the source characterisation of E signals associated with a defective bearing whilst in operation.