RESEARCH PAPER
Application of machine learning and rough set theory in lean maintenance decision support system development
| 1 | Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Powstańców Warszawy 8, 35-959 Rzeszów, Poland |
| 2 | Poznan University of Technology, Faculty of Management Engineering, Prof. Rychlewskiego 2, 60-965 Poznan, Poland |
| 3 | Beihang University (BUAA), School of Automation Science and Electrical Engineering, 37 Xueyuan Road, Beijing, 100191, China |
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):695–708
HIGHLIGHTS
- A review of lean maintenance importance in manufacturing.
- A approach with rough set theory and decision tree.
- Rough set theory with different types of algorithms selected for predictive models.
- The classification model for lean maintenance implementation assessment.
KEYWORDS
ABSTRACT
Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.
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