ICTACT Journal on Soft Computing

B. Selvalakshmi ¹, P. Vijayalakshmi ², N Subha ², T Balamani ³

Affiliations
1 Department of Computer Science and Engineering, Tagore Engineering College
2 Department of Computer Science and Engineering, Knowledge Institute of Technology, IN
3 Department of Electronics and Communication Engineering, M. Kumarasamy college of Engineering, IN

Abstract

In industrial systems, predictive maintenance has emerged as a crucial strategy to minimize downtime and optimize operational efficiency. This study explores the utilization of data mining techniques, specifically fuzzy logic systems, for predictive maintenance. The background section examines the importance of predictive maintenance in industrial contexts and highlights the limitations of traditional approaches. The methodology section outlines the process of employing fuzzy logic systems for predictive maintenance, including data preprocessing, feature selection, fuzzy rule generation, and model evaluation. The contribution of this research lies in providing a comprehensive framework for implementing predictive maintenance using fuzzy logic systems, offering insights into the integration of data mining techniques with industrial systems. Results demonstrate the effectiveness of the proposed methodology in accurately predicting maintenance needs and minimizing unplanned downtime. Findings suggest that fuzzy logic systems can enhance predictive maintenance capabilities by handling uncertainties and vagueness inherent in industrial data.

Keywords

Predictive Maintenance, Industrial Systems, Data Mining, Fuzzy Logic Systems, Operational Efficiency

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