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Analysis of Breakdown Reports using Natural Language Processing and Machine Learning

Publication Type:

Conference/Workshop Paper


International Congress and Workshop on Industrial AI


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 study, it was concluded though that none of these methods were able to find any suitable hidden patterns that could be related to recurring failures and its 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. 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.


author = {Mobyen Uddin Ahmed and Marcus Bengtsson and Antti Salonen and Peter Funk},
title = {Analysis of Breakdown Reports using Natural Language Processing and Machine Learning},
month = {August},
year = {2021},
booktitle = {International Congress and Workshop on Industrial AI },
url = {}