Predictive Maintenance in Mechanical Systems Using IoT and Machine Learning

Abstract

Mechanical systems are critical components in manufacturing, transportation, energy, and infrastructure. Traditional maintenance strategies, such as reactive and preventive maintenance, often lead to unplanned downtime, high operational costs, and inefficiency. Predictive maintenance (PdM) leverages IoT sensors, data acquisition systems, and machine learning algorithms to predict failures before they occur, optimizing maintenance schedules and minimizing disruptions. This paper presents a comprehensive study on the integration of IoT and machine learning for predictive maintenance in mechanical systems. The paper reviews IoT-enabled data acquisition, feature extraction, machine learning models, real-time monitoring frameworks, and deployment strategies. Simulation and case studies demonstrate that IoT-based predictive maintenance improves system reliability, reduces maintenance costs by 20–40%, and enhances operational efficiency. Challenges such as data quality, model interpretability, and cybersecurity are also discussed.

Cite as:

Dr. Kondekal Manjunatha. (2025). Predictive Maintenance in Mechanical Systems Using IoT and Machine Learning. Recent Trends in Automation and Automobile Engineering, 8(3), 44–48. 

https://doi.org/10.5281/zenodo.17650464