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Givnan, S, Chalmers, C, Fergus, P, Ortega-martorell, S and Whalley, T (2022) Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors. Sensors, 22 (9). p. 3166. ISSN 1424-8220
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Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a machine learning (ML) approach to model normal working operations and detect anomalies. The approach extracts key features from signals representing a known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system where green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | anomaly detection; autoencoder; condition monitoring; data filtering; edge computing; machine learning; predictive maintenance; real-time monitoring; rotary machine; windowed data; Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0301 Analytical Chemistry; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Divisions: | Computer Science & Mathematics |
| Publisher: | MDPI AG |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 23 May 2022 12:58 |
| Last Modified: | 23 May 2022 13:00 |
| DOI or ID number: | 10.3390/s22093166 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/16907 |
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