Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop t...

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Bibliographic Details
Published in:Journal of physics. Conference series 2017-09, Vol.892 (1), p.12008
Main Authors: Kwon, Jung-Hyok, Lee, Sol-Bee, Park, Jaehoon, Kim, Eui-Jik
Format: Article
Language:English
Subjects:
Algorithms
Failure
Industrial applications
Internet of Things
Physics
Prediction models
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Summary:This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users' understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/892/1/012008