ArticleSensors (Basel, Switzerland)2022
Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Advanced Sensor Technologies in Cutting Applications: A Review.Sensors (Basel, Switzerland) · 2026Review
- Crack Monitoring in Rotating Shaft Using Rotational Speed Sensor-Based Torsional Stiffness Estimation with Adaptive Extended Kalman Filters.Sensors (Basel, Switzerland) · 2023Article
- Degradation Feature Extraction Method for Prognostics of an Extruder Screw Using Multi-Source Monitoring Data.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
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