ER-RATBY, Mohamed (LASTI, Laboratory of Science and Engineering Techniques National), KOBI, Abdessamad (University of Angers), SADRAOUI, Youssef (LIPIM, Laboratory of Process Engineering, Computer Science, and ), KADIRI, Moulay Saddik (LIPIM, Laboratory of Process Engineering, Computer Science, and )

Predictive Maintenance: A Comparative Study of Machine Learning Algorithms in Industrial Applications

Scheduled for presentation during the Regular Session "Fault diagnosis II" (WeBC), Wednesday, June 11, 2025, 15:20−15:40, Room B

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

In an industrial context, monitoring machine conditions is essential to ensure regular production and maintain equipment. Traditional approaches, such as reactive and preventive maintenance, often prove to be inefficient in terms of resource and time management. This study explores the application of predictive maintenance in companies, leveraging data science and machine learning techniques. Predictive maintenance anticipates failures by analyzing real-time collected data using advanced algorithms. This reduces unplanned downtimes, optimizes interventions, and improves resource management, thereby increasing equipment availability and operational performance. A comparison of machine learning algorithms, including decision trees, XGBoost, SVM, KNN, logistic regression, Gaussian Naive Bayes, and random forests, shows that XGBoost offers superior classification accuracy. Consequently, this model was chosen to develop a user-friendly application that allows users to easily monitor machine health. Keywords: Predictive maintenance, Industry 4.0, Diagnostics, Decision tree algorithms, Machine Learning, Internet of Things, Smart manufacturing, Logistic regression, Gaussian Naive Bayes, Random forest.