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Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors
Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults.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 threshol...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103022/ https://www.ncbi.nlm.nih.gov/pubmed/35590855 http://dx.doi.org/10.3390/s22093166 |
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author | Givnan, Sean Chalmers, Carl Fergus, Paul Ortega-Martorell, Sandra Whalley, Tom |
author_facet | Givnan, Sean Chalmers, Carl Fergus, Paul Ortega-Martorell, Sandra Whalley, Tom |
author_sort | Givnan, Sean |
collection | PubMed |
description | Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults.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. |
format | Online Article Text |
id | pubmed-9103022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91030222022-05-14 Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors Givnan, Sean Chalmers, Carl Fergus, Paul Ortega-Martorell, Sandra Whalley, Tom Sensors (Basel) Article 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. MDPI 2022-04-20 /pmc/articles/PMC9103022/ /pubmed/35590855 http://dx.doi.org/10.3390/s22093166 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Givnan, Sean Chalmers, Carl Fergus, Paul Ortega-Martorell, Sandra Whalley, Tom Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors |
title | Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors |
title_full | Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors |
title_fullStr | Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors |
title_full_unstemmed | Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors |
title_short | Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors |
title_sort | anomaly detection using autoencoder reconstruction upon industrial motors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103022/ https://www.ncbi.nlm.nih.gov/pubmed/35590855 http://dx.doi.org/10.3390/s22093166 |
work_keys_str_mv | AT givnansean anomalydetectionusingautoencoderreconstructionuponindustrialmotors AT chalmerscarl anomalydetectionusingautoencoderreconstructionuponindustrialmotors AT ferguspaul anomalydetectionusingautoencoderreconstructionuponindustrialmotors AT ortegamartorellsandra anomalydetectionusingautoencoderreconstructionuponindustrialmotors AT whalleytom anomalydetectionusingautoencoderreconstructionuponindustrialmotors |