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.

Automated summary

This paper proposes a knowledge-based data analysis algorithm for health condition monitoring in rotating machinery, specifically targeting the early detection of subsurface cracks induced by contact fatigue. A robust fault detector is introduced and its efficacy is demonstrated through a long-term durability test on a case-hardened steel roller. The reliability of subsurface crack detection is confirmed by independent periodic ultrasonic inspections. Subsurface cracks as small as 0.5mm are identified and their steady growth is tracked using the proposed acoustic emission (AE) technique. Challenges and future prospects of this methodology are discussed.