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Title: Damage detection of rotating machinery
Author: Cockerill, Aaron
ISNI:       0000 0004 6424 9331
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2017
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Acoustic emission (AE) is an emerging technique for the condition monitoring of rotating machinery components, including both rolling element bearings and gears. Due to the high frequency range over which AE is sensitive to, AE potentially offers advantages for detection of incipient damage at an early stage of failure when compared to traditional techniques such as vibration. This thesis first investigates the effects of increased speed and load on the generation of AE within cylindrical roller bearings, and determines similarities and differences between AE and vibrational data. A traditional AE sensor was used in conjunction with a Dual Function Sensor (DFS) capable of recording both low frequency AE and vibration. It was shown that increasing speed has the greatest influence on the AE signals produced whereas the effect of load was limited. Order analysis of both AE and vibrational data also demonstrated that characteristic bearing defect frequencies are visible in the AE spectrum but not in the vibrational spectrum. Bearings with seeded defects upon the outer raceway were investigated under a fixed speed and it was found that load increased the energy within the signal frequency spectrum as the damaged increased. Two bearing life tests were also conducted, one accelerated to 12 hours and the second extended to over 2800 hours however as damage detection only occurred after significant damage had developed, it is concluded that AE of seeded defects indicate a false sensitivity. Both life tests were able to demonstrate that signal levels increase as damage propagates over the bearing raceway however it was not possible to determine any advantage of using AE over vibration. AE sensors were also applied to test rigs of increased complexity, including the monitoring a wind turbine planet bearing and a helical gear pair. AE was able to detect cracking of the shaft surface within the wind turbine bearing test rig which was mistaken for being an inner raceway failure, highlighting the difficulty in damage location. A tooth failure occurred during the testing of the helical gear pair however AE was not able to detect growing damage, instead only increasing in amplitude after the tooth had sheared off, similar to the detection from vibrational signals.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available