Acoustic emission monitoring of rolling element bearings failures

Acoustic emission (AE) is a condition monitoring technique used for rotating machinery components that is gaining ground in the industrial field, due to its sensitivity to high frequency range, which makes it advantageous compared with traditional vibration techniques in the detection of incipient damage at the early stages of failure. This thesis presents an investigation using three types of similarly-sized cylindrical roller bearings, yet with different characteristics and qualities on a high speed test rig, with maximum rotational speed set at 5980rpm. The bearings under investigation are normal clearance SKF Types NU202ECP and NU202EM budget bearing, NU202ECP/C3 with higher radial clearance. This work aims to investigate the healthy bearings characterisation tests to deliver a well-defined foundation of the AE signal results, as well their operational lubrication regimes. Secondly, to investigate the underlying early stages and presence of naturally propagated damage within a rolling element bearing under heavily loaded operational conditions. Lastly, to compare the results from the 3 types of bearings. The healthy bearing characterisation tests identified that load and speed influenced the generated AE signal, with speed having a greater impact on the AE signal. It has also shown that the higher radial clearance bearing generated lower levels of energy excitation. The experiments principally operated within the hydrodynamic lubrication regime for both SKF bearings, while within the mixed lubrication regime for the budget bearing. The Run to Failure tests were then conducted to replicate natural failures in an accelerated yet controlled manner, by reducing the life expectancy of a roller bearing through exceeding the specified operational limits. It was shown that as the damage propagates, the AE signal levels increase. Based upon the 3 types of bearings adopted for the test, maximum AE activities indicating failure emerged into the life span of 66 hours for SKF NU202ECP, 88 hours for SKF NU202ECP/C3 and 22 hours for NU202EM. It is concluded that the data analysis process characteristically showed that the AE RMS signal is a robust technique and capable of perceiving present damage within the rolling element bearing

Damage detection of rotating machinery

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