Artificial Intelligence for Predictive Maintenance in Engineering Systems

Dr Saima Shaikh Mentor Head, Dept of IT Director, Care Maharashtra College of Arts, Science and Commerce

Suha Fitwala Research Scholar Dept of IT Maharashtra College of Arts, Science and Commerce

Aaisha Fitwala Research Scholar Dept of IT Maharashtra College of Arts, Science and Commerce

Abstract :

Predictive maintenance marks a shift from traditional time-based and reactive maintenance strategies. It uses data analytics and machine intelligence to foresee equipment failures before they happen. This method employs algorithms to analyze data from sensors, identify signs of wear, and predict when components will fail. This study looks into how artificial intelligence techniques, such as machine learning, deep learning, and ensemble methods, can create effective predictive maintenance systems for complex engineering setups. The research covers important aspects of AI-driven maintenance, including how to collect sensor data, advanced preprocessing methods, developing intelligent models, implementing these systems in real-world settings, evaluating performance, and analyzing results. Additionally, this paper discusses key technical and operational challenges in deploying AI-based predictive maintenance, such as ensuring data quality, making models understandable, integrating systems, and scalability issues.
Keywords: Artificial Intelligence, Predictive Maintenance, Engineering Systems, Machine Learning, Deep Learning, Sensor Analytics, Fault Prediction, IoT Integration, Explainable AI, Digital Twins

How to cite?

Shaikh, S., Fitwala, S., & Fitwala, A. (2025). Artificial intelligence for predictive maintenance in engineering systems. myresearchgo, 1(7), 79.