Explainable Machine Learning for Predictive Maintenance of Industrial Machines: A Comparative Study on Failure Prediction

This paper presents a comparative study of seven machine learning algorithms for predicting industrial machine failures using a simulated dataset. It evaluates model performance and applies explainable AI techniques to identify key failure factors, including mechanical power, tool wear, and torque. The study highlights the potential of explainable machine learning to improve predictive maintenance and support reliable industrial decision-making.