Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission

Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness was demonstrated for the long-term durability test of a roller made of case-hardened steel. The reliability of subsurface crack detection was proven using independent ultrasonic inspections carried out periodically during the test. Subsurface cracks as small as 0.5 mm were identified, and their steady growth was tracked by the proposed AE technique. Challenges and perspectives of the proposed methodology are unveiled and discussed.publishedVersio

Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission

Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness was demonstrated for the long-term durability test of a roller made of case-hardened steel. The reliability of subsurface crack detection was proven using independent ultrasonic inspections carried out periodically during the test. Subsurface cracks as small as 0.5 mm were identified, and their steady growth was tracked by the proposed AE technique. Challenges and perspectives of the proposed methodology are unveiled and discussed

Early Detection of Subsurface Cracks in Rolling Element Bearings using the Acoustic Emission Time Series

The formation and propagation of rolling contact fatigue (RCF) induced subsurface cracks (SSC) in a test specimen roller have been monitored using the acoustic emission time series. The sampled acoustic emission (AE) waveforms were obtained from a duration test. During testing, phased array ultrasonic testing (PAUT) were performed on scheduled intervals to monitor SSC initiation and growth. After the duration test was terminated, salami cutting post inspection revealed three RCF induced SSCs. A monitoring system using a mathematically deterministic detector, capable of independent isolated detection of multiple RCF induced SSCs occurring simultaneously in a rotating machinery is proposed in this thesis. Outputs from the detector and positive detector decisions, are fully verifiable using a tool proposed in this thesis called the pulse integrated spectrogram (PIS). Four different defect behaviours were observed in the sampled AE waveforms. All behaviours were independently detected with the proposed monitoring system. The behaviour with the given name rollerPass, was confirmed as an SSC originated behaviour. Positive detector decision, defect detected, for rollerPass happened April 30, 2021. The decision was verified with PIS. At the time of detection, the SSC was 1 mm wide, confirmed in PAUT. A review of the published research on the field that is detection of RCF induced SSCs in rolling element bearings (REB) using AE is presented in this thesis. The review reveals that unverifiable results can have caused false claims of success for the solutions presented. A criterion of confidence is therefore proposed to prevent future publications from disrupting the progress in this field of research

Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission

Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness was demonstrated for the long-term durability test of a roller made of case-hardened steel. The reliability of subsurface crack detection was proven using independent ultrasonic inspections carried out periodically during the test. Subsurface cracks as small as 0.5 mm were identified, and their steady growth was tracked by the proposed AE technique. Challenges and perspectives of the proposed methodology are unveiled and discussed