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Analysis of Breakdo...
Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning
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- Ahmed, Mobyen Uddin, Dr, 1976- (author)
- Mälardalens universitet,Inbyggda system
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- Bengtsson, Marcus, 1977- (author)
- Mälardalens universitet,Innovation och produktrealisering,Volvo Construction Equipment, Västerås, Sweden
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- Salonen, Antti (author)
- Mälardalens universitet,Innovation och produktrealisering
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- Funk, Peter, 1957- (author)
- Mälardalens universitet,Inbyggda system
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(creator_code:org_t)
- 2022-02-07
- 2022
- English.
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In: Lecture Notes in Mechanical Engineering. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030936389 ; , s. 40-52
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Computerized maintenance management system
- Machine learning
- Natural language processing
- Recurring breakdowns
- Root cause failure analysis
Publication and Content Type
- ref (subject category)
- kon (subject category)
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