Machine learning-based predictive maintenance: enhancing ‎industrial reliability through data-driven approaches

  • Authors

    • S.J. Subhashini Department of Computer Science and Engineering, SRM Madurai College for Engineering and Technology, Sivagangai, India
    • Syed Asif Basha Department of Computer Engineering, College of Computer Science,King Khalid University, Abha-614111,K. S., A.
    • B. Srinivasa Rao Department of Computer Science and Engineering (Data Science)، Geethanjali College of Engineering and Technology, Cheeryal, Med‎chal, Hyderabad, Telangana -501301, India
    • S. Gayathri Department of Artificial Intelligence and Data Science, K.Ramakrishnan College of Engineering, Tiruchirappalli, India
    • Amol Mangrulkar MCT's Rajiv Gandhi Institute of Technology, Mumbai, India
    https://doi.org/10.14419/05tz0p10

    Received date: March 18, 2025

    Accepted date: May 1, 2025

    Published date: May 22, 2025

  • Data-Driven Maintenance; Failure Prediction; Industrial Equipment; Machine Learning; Predictive Maintenance

  • Abstract

    This study investigated the application of machine learning for predictive maintenance (PM) using synthetic data simulating industrial ma-‎chinery failures. Different algorithms including random forest, support vector machine (SVM), artificial neural network (ANN), decision ‎tree (DT), and logistic regression (LR) were evaluated in two test scenarios. Decision tree (DT) and logistic regression (LR) showed the ‎best promise, despite challenges with data imbalance and data segmentation. However, these models are not yet suitable for industrial de-‎ployment due to the significant impact of misclassified faults. The results highlight the potential of machine learning to improve predictive ‎maintenance (PM), while further improvements are needed before it can replace human supervision‎.

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  • How to Cite

    Subhashini, S., Basha, S. A. ., Rao, B. S. ., Gayathri, S., & Mangrulkar , A. . (2025). Machine learning-based predictive maintenance: enhancing ‎industrial reliability through data-driven approaches. International Journal of Basic and Applied Sciences, 14(1), 339-349. https://doi.org/10.14419/05tz0p10