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

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.