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Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Volvo Construction Equipment, Västerås, Sweden.ORCID iD: 0000-0002-0729-0122
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-7494-1474
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5562-1424
2022 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2022, p. 40-52Conference paper, Published paper (Refereed)
Abstract [en]

Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2022. p. 40-52
Keywords [en]
Computerized maintenance management system, Machine learning, Natural language processing, Recurring breakdowns, Root cause failure analysis
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-57576DOI: 10.1007/978-3-030-93639-6_4ISI: 000777604600004Scopus ID: 2-s2.0-85125258793ISBN: 9783030936389 (print)OAI: oai:DiVA.org:mdh-57576DiVA, id: diva2:1643246
Conference
International Congress and Workshop on Industrial AI, IAI 2021 Virtual, Online 6 October 2021 through 7 October 2021 Code 272219
Available from: 2022-03-09 Created: 2022-03-09 Last updated: 2022-06-07Bibliographically approved

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Ahmed, Mobyen UddinBengtsson, MarcusSalonen, AnttiFunk, Peter

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