A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this public...

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Autores principales:	Corbinian Nentwich, Gunther Reinhart
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	condition monitoring industrial robots anomaly detection trend detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/1ade47a3f0a345c09b9292db1dcc4743
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spelling	oai:doaj.org-article:1ade47a3f0a345c09b9292db1dcc47432021-11-11T15:24:03ZA Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring10.3390/app1121104032076-3417https://doaj.org/article/1ade47a3f0a345c09b9292db1dcc47432021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10403https://doaj.org/toc/2076-3417Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.Corbinian NentwichGunther ReinhartMDPI AGarticlecondition monitoringindustrial robotsanomaly detectiontrend detectionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10403, p 10403 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	condition monitoring industrial robots anomaly detection trend detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	condition monitoring industrial robots anomaly detection trend detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Corbinian Nentwich Gunther Reinhart A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
description	Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.
format	article
author	Corbinian Nentwich Gunther Reinhart
author_facet	Corbinian Nentwich Gunther Reinhart
author_sort	Corbinian Nentwich
title	A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
title_short	A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
title_full	A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
title_fullStr	A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
title_full_unstemmed	A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
title_sort	combined anomaly and trend detection system for industrial robot gear condition monitoring
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1ade47a3f0a345c09b9292db1dcc4743
work_keys_str_mv	AT corbiniannentwich acombinedanomalyandtrenddetectionsystemforindustrialrobotgearconditionmonitoring AT guntherreinhart acombinedanomalyandtrenddetectionsystemforindustrialrobotgearconditionmonitoring AT corbiniannentwich combinedanomalyandtrenddetectionsystemforindustrialrobotgearconditionmonitoring AT guntherreinhart combinedanomalyandtrenddetectionsystemforindustrialrobotgearconditionmonitoring
_version_	1718435353697714176

A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

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