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

¹ Professor, Department of Computer Science and Engineering, CVR College of Engineering, Hyderabad, Telangana, India
² Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India
³ Assistant Professor, Department of Mechanical Engineering, BGSIT, Faculty of Engineering Management and Technology, Adichunchanagiri University, Bgnagara - 571448, Nagamangala (Tq), Mandya(Dis), Karnataka, India
⁴ Assistant professor, Department of Industrial Engineering and Management, Bangalore Institute of Technology, Bangalore, Karnataka, India
⁵ Professor, CSE, J.J. College of Engineering and Technology, Trichy, Tamil Nadu, India
⁶ Professor, Department of EEE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

drddurgabhavani@gmail.com
nagarjunatandra@mlrit.ac.in
pradeephgowda@gmail.com
preethinarayanabit@gmail.com
ravir@jjcet.ac.in
senthilkumar@newprinceshribhavani.com

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.