Extracting failure time data from industrial maintenance records using text mining

• Published in: Advanced engineering informatics 2017-08, Vol.33, p.388-396
• Main Authors: Arif-Uz-Zaman, Kazi, Cholette, Michael E., Ma, Lin, Karim, Azharul
• Format: Article
• Language: English
• Subjects: Naïve Bayes; Support vector machine; Text mining; Work orders analysis
• ISSN: 1474-0346
• DOI: 10.1016/j.aei.2016.11.004

Reliability modelling requires accurate failure time of an asset. In real industrial cases, such data are often buried in different historical databases which were set up for purposes other than reliability modelling. In particular, two data sets are commonly available: work orders (WOs), which detail maintenance activities on the asset, and downtime data (DD), which details when the asset was taken offline. Each is incomplete from a failure perspective, where one wishes to know whether each downtime event was due to failure or scheduled activities. In this paper, a text mining approach is proposed to extract accurate failure time data from WOs and DD. A keyword dictionary is constructed using WO text descriptions and classifiers are constructed and applied to attribute each of the DD events to one of two classes: failure or nonfailure. The proposed method thus identifies downtime events whose descriptions are consistent with urgent unplanned WOs. The applicability of the methodology is demonstrated on maintenance data sets from an Australian electricity and sugar processing companies. Analysis of the text of the identified failure events seems to confirm the accurate identification of failures in DD. The results are expected to be immediately useful in improving the estimation of failure times (and thus the reliability models) for real-world assets.