Machine Learning Approaches for Predictive Maintenance in Industrial Operations

Predictive maintenance (PdM) has emerged as a transformative approach for enhancing industrial efficiency and reliability, leveraging machine learning (ML) techniques for real-time data analysis and anomaly detection. This study proposes a hybrid PdM framework integrating advanced ML models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Autoregressive Integrated Moving Average (ARIMA), Isolation Forest, and One-Class SVM. Using IoT-enabled sensor data capturing parameters like vibration, temperature, and pressure, the framework demonstrates significant improvements. Key findings reveal that CNNs achieved 92% accuracy in early anomaly detection, while the combined LSTM-ARIMA model provided forecasting accuracy of 87% and reduced errors by 25% compared to standalone models. Isolation Forest and One-Class SVM achieved anomaly detection precision of 93% and recall of 88%. The framework reduced unplanned downtime by 54% and maintenance costs by 32% across tested scenarios. This study contributes to the existing body of knowledge by presenting a robust hybrid modeling approach that integrates statistical and deep learning methods to address fault prediction and maintenance scheduling limitations. Practical implications include improved maintenance efficiency, extended equipment lifespan, and enhanced industry decision-making capabilities, making the framework highly relevant for Industry 4.0 applications.