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Data-Driven Gearbox Failure Detection in Industrial Robots
ABB Abil Innovat Ctr, India; Indraprastha Inst Informat Technol Delhi, India.
ABB Corp Res, Poland.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. ABB Robot Business Line, Sweden.ORCID iD: 0000-0002-2100-6378
Northern Illinois Univ, IL 60115 USA.
2020 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 1, p. 193-201Article in journal (Refereed) Published
Abstract [en]

Gearbox failures cost thousands of lost production hours in plants that use industrial robots. In this context, an automated monitoring system that can warn the user of an impending failure can save precious resources. This problem has been addressed in many other domains through the use of machine learning approaches. However, standard machine learning algorithms are limited in their ability to detect gearbox failures, mainly due to task variability arises from robot-specific data. To improve detection performance of machine learning approaches, in this paper we propose techniques to curate the data prior to building a classification model. In a systematic hypothesis-driven study exploring the effect of different preprocessing techniques, we evaluate training data augmentation with estimated measurements, data differencing to suppress task dependence, inclusion of local variation, and selection of principal components on data collected from 26 industrial robots from the field. Our results show that preprocessing techniques improve the failure detection performance.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2020. Vol. 16, no 1, p. 193-201
Keywords [en]
Service robots; Training data; Support vector machines; Torque; Machine learning algorithms; Task analysis; Classification methods; data-driven methods; industrial robots; preprocessing
National Category
Robotics
Identifiers
URN: urn:nbn:se:liu:diva-163465DOI: 10.1109/TII.2019.2912809ISI: 000508428900018OAI: oai:DiVA.org:liu-163465DiVA, id: diva2:1393684
Note

Funding Agencies|ABB, Ability Innovation Center, Bangalore

Available from: 2020-02-17 Created: 2020-02-17 Last updated: 2021-12-06

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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