Planning Maintenance and Repairs Using the Combination of Data Mining Techniques and Time Series

Document Type : IIIEC 2025

Authors

1 Department of Industrial Engineering, Bon.C., Islamic Azad University, Bonab, Iran

2 ie

3 Ph.D. Candidate of Department of Industrial Engineering, Faculty of Industrial Engineering and Management, Malek Ashtar University of Technology, Tehran, Iran,

Abstract

Objective: The primary objective of this study is to develop an effective framework for preventive maintenance and repair planning aimed at reducing maintenance costs, minimizing equipment downtime, and optimizing overtime shifts. By integrating data mining techniques with time series analysis, the research seeks to support managerial decision-making through accurate prediction of repair types, failure times, and operational team performance.
Methods: In this research, clustering algorithms were first employed to classify equipment failures based on similarities in maintenance and repair activities. Subsequently, association rule mining was applied to extract descriptive rules for each cluster and to define effective ranges for influential factors. Time series models were then utilized to predict the time periods during which these factors satisfy the extracted rule ranges. A comprehensive and validated database was constructed using six months of real maintenance records, including failure types, repair operations, equipment downtime, and repair team information.
Results: The results demonstrate that the proposed hybrid approach enables accurate prediction of repair types, equipment downtime, and workforce productivity. By comparing the extracted association rules with time series forecasts, a novel preventive planning mechanism was established. The findings reveal significant differences in the performance of repair teams and indicate that merging or eliminating certain shifts can lead to a substantial reduction in maintenance and repair costs without compromising system reliability.
Conclusion: This study confirms that the integration of data mining techniques and time series analysis provides a robust and practical approach for preventive maintenance and repair planning. The proposed framework enhances resource allocation, reduces unplanned downtime, and supports cost-effective operational strategies. With accurate data collection and continuous database updating, the method can be effectively implemented in manufacturing environments as a decision-support tool for maintenance management.

Keywords

References

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Management Science and Information Technology
Volume 2, Issue 3
September 2025
Pages 247-268

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