This study presents an approach for the detection of evolving degradation in large-scale low-speed roller bearings by clustering of Acoustic Emission (AE) events, and its application to experimental degradation data. To acquire the latter, a purpose-built linear bearing, representative of a segment of a turret bearing, has been instrumented with multiple piezoelectric AE transducers in the frequency range between 40–580 kHz. Clustering based on cross-correlation has identified a number of significant clusters that are linked to the observed damage. The results suggest that condition monitoring based on AE waveform similarity clustering is suitable for detection and identification of degradation in a large-scale roller bearing.
|Name||Lecture Notes in Civil Engineering|
|Conference||10th European Workshop on Structural Health Monitoring, EWSHM 2022|
|Period||4/07/22 → 7/07/22|
Accepted Author Manuscript
- Acoustic Emission
- Condition Monitoring
- Roller Bearing