Application of Machine Learning for Predictive Maintenance in Industrial Systems

Predictive maintenance (PdM) in industrial systems seeks to forecast equipment failures before they occur, thereby reducing unplanned downtime, maintenance costs, and safety risks. Leveraging machine learning (ML) techniques—such as decision trees, support vector machines (SVMs), random forests, and artificial neural networks (ANNs)—enables the analysis of vast sensor‑generated datasets for early fault detection and remaining useful life (RUL) estimation. This manuscript presents a comprehensive framework for applying ML to PdM within manufacturing and energy sectors, focusing on data acquisition, feature extraction, model training, and evaluation. We compare classical ML methods (Random Forest, SVM, ANN) against deep learning approaches (Long Short‑Term Memory networks, CNN‑based classifiers) using a benchmark rolling bearing dataset. A detailed statistical analysis, including one table of performance metrics, demonstrates that deep learning methods can achieve up to 92% classification accuracy and an F1‑score of 0.90, surpassing classical techniques in both predictive accuracy and robustness to noisy data. We also explore dimensionality reduction via principal component analysis (PCA) to balance computational efficiency and model performance. Finally, we discuss scope and limitations—highlighting data quality concerns, sensor heterogeneity, and computational costs—and offer practical guidelines for implementing PdM solutions with technologies available up to 2022. The findings underscore ML‑driven strategies’ potential to transform maintenance practices, improve operational reliability, and inform best practices for engineering deployment. In addition to demonstrating superior classification performance, this study provides a clear roadmap for practitioners selecting and tuning ML pipelines under real‑world constraints such as limited labeled data, variable operating environments, and heterogeneous sensor platforms. We delve into the practicalities of deploying ML models on edge devices versus centralized cloud infrastructure, examining trade‑offs in latency, throughput, and model update cycles. We also outline strategies for incremental learning to accommodate evolving fault modes without full retraining. By anchoring all techniques to peer‑reviewed studies and industrial case examples through 2022, this work ensures relevance for engineers and decision-makers tasked with modernizing maintenance operations in sectors ranging from automotive assembly to power plant management.