Machine Learning for Predictive Maintenance Applications in Industrial Equipment and Manufacturing Processes

Abstract

Utilization of predictive maintenance, backed by machine learning, has made a difference in monitoring industrial equipment and manufacturing, cutting down on downtime, improving operational efficiency, and ensuring safety. However, current systems suffer limitations, including lack of real-time deployment, low scalability, significant computation footprints, security vulnerabilities and low interpretability. We present a novel, scalable explainable AI based predictive maintenance framework integrating lightweight deep learning models, federated learning, blockchain secure storage and adaptive self-learning mechanisms. With the application of edge AI computing, interpretable machine learning methods, and real-time industrial data processing, the proposed study realizes a cost-effective, secure, and scalable predictive maintenance solution. A practical and innovative solution for minimizing failures and enhancing manufacturing efficiency involving sustainable smart industrial approaches can be achieved by validating the proposed model in real-world industrial environments.

Keywords

• latest trends & technologies in edge ai for predictive maintenance: deep learning
• federated learning
• explainable ai
• blockchain security
• real-time failure detection
• cyber-physical systems