Learning-Based Algorithm for Fault Prediction Combining Different Data Mining Techniques: A Real Case Study
Abstract
In recent years, new Data Mining (DM) algorithms and methodologies are increasingly used as an
industrial solution for manufacturing improvements. In this context, new techniques are widely required by
companies in the field of maintenance due to the need to reduce breakdowns intervention and take advantage of
the increasing availability of data. This paper aims to propose a new learning-based algorithm to improve
knowledge extraction by combining different DM techniques from a predictive maintenance perspective. First,
the J48 algorithm and Random Forest (RF) are used as a predictive model to classify a set of failure modes
according to their influence on the Overall Equipment Effectiveness (OEE). Then, the Apriori algorithm is used
to identify the relationship among failure events belonging to the lowest OEE range for which, therefore, a
predictive maintenance strategy should be defined. In order to describe the learning-based algorithm proposed in
this paper, a real case study is presented and detailed. The experimental results showed a valuable tool for
knowledge extraction and the definition of a set of predictive maintenance strategies for those failures most
affecting the process. In this way, the complexity of decision-making on maintenance strategies can be reduced
mainly when dealing with a large amount of information or a challenging dataset.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | December 31, 2022 |
| Published in Issue | Year 2022 |
