Automl-Based Predictive Maintenance Model for Accurate Failure Detection

This study focuses on predictive maintenance, a critical maintenance policy that benefits from the development of the Digital Twin (DT) philosophy. To implement predictive maintenance, it is essential to predict potential failures. In this study, machine learning algorithms are used to detect failure conditions. Five different types of failures are classified by examining parameters such as air temperature, process temperature, rotation speed, torque, and tool wear. The study utilizes Automatic Machine Learning (AutoML), which runs machine learning algorithms and returns the best method, its hyperparameters, and many outputs, such as accuracy and performance metrics. The literature on machine learning algorithms in predictive maintenance has focused on finding the best algorithm by applying selected methods. However, this study aims to contribute to the literature by finding the algorithm that provides the best results among all methods using AutoML in predictive maintenance.