DefinePK

Abstract

Predictive maintenance leverages data-driven approaches to foresee equipment failures and reduce downtime in industrial settings. This study evaluates the performance of several machine learning (ML) and deep learning (DL) models on the AI4I 2020 synthetic dataset, which simulates a milling machine's operational conditions and failure types. Models including Random Forest, Support Vector Machine (SVM), XGBoost, and a deep neural network were assessed using standard classification metrics such as Accuracy, F1-Score, Precision, and Recall. Surprisingly high F1-scores, often exceeding 0.995, were achieved across all classifiers and failure types. This exceptional performance is attributed to the dataset's high quality, clear feature-label relationships, and absence of noise. We analyze the implications of such results, highlighting potential limitations in model generalization to real-world scenarios. The study underscores the importance of dataset characteristics, model selection, and validation strategies in predictive maintenance applications. Practical insights and guidelines are provided to support the deployment of such models in industrial environments, with emphasis on validation against real-world conditions and robustness testing.