GGLCM: A Real-time Early Anomaly Detection Method for Mechanical Vibration Data with Missing Labels
Early anomaly detection plays a central role in the scientific maintenance of mechanical equipment. Although the application is limited by weak monitoring, it encounters the problem of missing labels. To overcome this challenge, the Gramian gray level co-occurrence matrix (GGLCM) analysis method is...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2025-07-01
|
| Series: | Measurement Science Review |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/msr-2025-0019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Early anomaly detection plays a central role in the scientific maintenance of mechanical equipment. Although the application is limited by weak monitoring, it encounters the problem of missing labels. To overcome this challenge, the Gramian gray level co-occurrence matrix (GGLCM) analysis method is proposed, which includes three phases: first, the time-series are input into the Gramian angular field (GAF) in real time for signal dimension reconstruction. Second, the gray level co-occurrence matrix (GLCM) is applied to the reconstructed signal. Since the GAF preserves the dependencies in the time-series, the limitation of missing labels is significantly weakened. Third, a continuous alarm mechanism is developed for reliable detection. Finally, the GGLCM is verified by actual vibration datasets of overloaded bearings. |
|---|---|
| ISSN: | 1335-8871 |