Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors



Givnan, Sean, Chalmers, Carl ORCID: 0000-0003-0822-1150, Fergus, Paul ORCID: 0000-0002-7070-4447, Ortega-Martorell, Sandra ORCID: 0000-0001-9927-3209 and Whalley, Tom
(2022) Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors. SENSORS, 22 (9). 3166-.

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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Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults.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, edge computing, autoencoder, predictive maintenance, condition monitoring, rotary machine, real-time monitoring, machine learning, data filtering, windowed data
Divisions: Faculty of Health and Life Sciences
Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 18 May 2023 09:16
Last Modified: 18 May 2023 09:16
DOI: 10.3390/s22093166
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3170470