Analysis of short form maintenance records for NFF using NLP, phrase matching, and Bayesian learning
Date
2017-03-02
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Publisher
Elsevier
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Article
ISSN
2212-8271
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Citation
Pelham J, Hockley C, Analysis of short form maintenance records for NFF using NLP, phrase matching, and Bayesian learning, Procedia CIRP, Volume 59, 2017, Pages 257-262
Abstract
No Fault Found (NFF) is a well discussed phenomenon within the maintenance sector but which requires work to quantify how much of an issue it may be and provide metrics by which it may be tracked and various approaches to its reduction evaluated. Previous studies have relied on expert classification to identify NFF, however this approach is time consuming and costly. Maintainer classification (MC), expert classification (RC), phrase matching (PM), and Bayesian matching (NBPM) are all evaluated and contrasted as methods to identify NFF. The results demonstrate the utility of all 4 methods and discusses their place within a maintenance ecosystem.
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Keywords
NFF, No Fault Found, NLP, Maintenance, Bayes
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Attribution 4.0 International (CC BY 4.0)
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