Machine Learning for Predictive Maintenance in Industrial Equipment: Challenges and Application

In the industrial sector, unplanned equipment failures can lead to significant financial losses, safety hazards, and operational inefficiencies. Traditional maintenance strategies, such as reactive maintenance (repair after failure) and preventive maintenance (scheduled maintenance), often prove inadequate in addressing these issues. This not only minimizes downtime but also optimizes maintenance resources and extends the lifespan of equipment. Machine learning models used in Predictive maintenance (PdM) are designed to analyze large volumes of data generated by industrial equipment. Reinforcement learning optimizes maintenance schedules by learning from interactions with the environment, balancing the trade-off between maintenance costs and equipment reliability. Black-box models, such as deep learning, often provide high accuracy but lack transparency. Explainable AI (XAI) techniques are being developed to make these models more interpretable, enabling users to gain insights into the decision-making process and build confidence in the predictions. The deployment of ML models in industrial environments also requires seamless integration with existing systems. This involves addressing issues related to real-time data processing, scalability, and model retraining. Edge computing and cloud-based solutions are being explored to handle large-scale data processing and storage, while online learning techniques allow models to adapt to new data continuously. This paper presents several case studies from industries such as manufacturing, energy, transportation, and healthcare, illustrating the successful application of ML in PdM. In manufacturing, ML models have been used to predict failures in machinery such as turbines, compressors, and conveyor belts, leading to significant reductions in downtime and maintenance costs. The energy sector has employed ML for the maintenance of critical infrastructure, including power grids and wind turbines, enhancing operational efficiency and reliability.