Intelligent vision based wear forecasting on surfaces of machine tool elements

Abstract To realize autonomous production machines it is necessary that machines are able to automatically and autonomously predict their condition. Although many classical as well as Deep Learning based approaches have shown the ability to classify faults, so far there are no approaches that go bey...

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Autores principales:	Tobias Schlagenhauf, Niklas Burghardt
Formato:	article
Lenguaje:	EN
Publicado:	Springer 2021
Materias:	Condition monitoring Predictive maintenance Machine vision Machine learning Object detection Wear of machine tool Science Q Technology T
Acceso en línea:	https://doaj.org/article/b66834143ba94587937f4fc8caaa4e52
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spelling	oai:doaj.org-article:b66834143ba94587937f4fc8caaa4e522021-11-14T12:15:17ZIntelligent vision based wear forecasting on surfaces of machine tool elements10.1007/s42452-021-04839-32523-39632523-3971https://doaj.org/article/b66834143ba94587937f4fc8caaa4e522021-11-01T00:00:00Zhttps://doi.org/10.1007/s42452-021-04839-3https://doaj.org/toc/2523-3963https://doaj.org/toc/2523-3971Abstract To realize autonomous production machines it is necessary that machines are able to automatically and autonomously predict their condition. Although many classical as well as Deep Learning based approaches have shown the ability to classify faults, so far there are no approaches that go beyond the basic detection of faults. A complete, image based predictive maintenance approach for machine tool components has to the best of our knowledge not been investigated so far. In this paper it is shown how defects on a Ball Screw Drive (BSD) can be automatically detected by using a machine learning based detection module, quantified by using an intelligent defect quantification module and finally forecasted by a prognosis module in a combined approach. To optimize the presented method, it is shown how existing domain knowledge can be formalized in an expert system and combined with the predictions of the machine learning model to aid quality of the prediction system. This enables the practitioner to forecast the severity of failures on BSD and prevent machine breakdowns. The work is intended to set new accents for the development of practical predictive maintenance systems and bridging the fields of machine learning and production engineering. The code is available under: https://github.com/2Obe/Pitting_Pred_Maintenance .Tobias SchlagenhaufNiklas BurghardtSpringerarticleCondition monitoringPredictive maintenanceMachine visionMachine learningObject detectionWear of machine toolScienceQTechnologyTENSN Applied Sciences, Vol 3, Iss 12, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Condition monitoring Predictive maintenance Machine vision Machine learning Object detection Wear of machine tool Science Q Technology T
spellingShingle	Condition monitoring Predictive maintenance Machine vision Machine learning Object detection Wear of machine tool Science Q Technology T Tobias Schlagenhauf Niklas Burghardt Intelligent vision based wear forecasting on surfaces of machine tool elements
description	Abstract To realize autonomous production machines it is necessary that machines are able to automatically and autonomously predict their condition. Although many classical as well as Deep Learning based approaches have shown the ability to classify faults, so far there are no approaches that go beyond the basic detection of faults. A complete, image based predictive maintenance approach for machine tool components has to the best of our knowledge not been investigated so far. In this paper it is shown how defects on a Ball Screw Drive (BSD) can be automatically detected by using a machine learning based detection module, quantified by using an intelligent defect quantification module and finally forecasted by a prognosis module in a combined approach. To optimize the presented method, it is shown how existing domain knowledge can be formalized in an expert system and combined with the predictions of the machine learning model to aid quality of the prediction system. This enables the practitioner to forecast the severity of failures on BSD and prevent machine breakdowns. The work is intended to set new accents for the development of practical predictive maintenance systems and bridging the fields of machine learning and production engineering. The code is available under: https://github.com/2Obe/Pitting_Pred_Maintenance .
format	article
author	Tobias Schlagenhauf Niklas Burghardt
author_facet	Tobias Schlagenhauf Niklas Burghardt
author_sort	Tobias Schlagenhauf
title	Intelligent vision based wear forecasting on surfaces of machine tool elements
title_short	Intelligent vision based wear forecasting on surfaces of machine tool elements
title_full	Intelligent vision based wear forecasting on surfaces of machine tool elements
title_fullStr	Intelligent vision based wear forecasting on surfaces of machine tool elements
title_full_unstemmed	Intelligent vision based wear forecasting on surfaces of machine tool elements
title_sort	intelligent vision based wear forecasting on surfaces of machine tool elements
publisher	Springer
publishDate	2021
url	https://doaj.org/article/b66834143ba94587937f4fc8caaa4e52
work_keys_str_mv	AT tobiasschlagenhauf intelligentvisionbasedwearforecastingonsurfacesofmachinetoolelements AT niklasburghardt intelligentvisionbasedwearforecastingonsurfacesofmachinetoolelements
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Intelligent vision based wear forecasting on surfaces of machine tool elements

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