Sensor-Fusion Based Intelligent System for Early Fault Prediction and Maintenance Optimization in Industrial Machinery
Sannapureddy Vijaya Lakshmi,
Praveen Tunga,
Mahesh Sadineni,
Venkata Krishna Kanth Paleru,
Gouse Shaik
Industrial machinery is extensively used in modern manufacturing environments, where continuous
operation is essential for productivity and safety. However, maintaining equipment reliability remains a
major challenge because of unexpected failures, wear, and operational stress. Traditional maintenance
methods such as reactive repair and preventive servicing are inefficient and often lead to excessive
downtime and cost. This paper proposes a sensor-fusion-based intelligent predictive maintenance
system that integrates multi-sensor data and learning-based fault prediction techniques into a unified
framework. The system continuously monitors machine opera-tional parameters such as temperature,
vibration, pressure, and load conditions to analyze equipment health in real time. By identifying
abnormal behavior and degradation patterns before failure, the proposed system generates automated
maintenance alerts and optimization recommendations. Experimental obser-vations indicate that the
proposed approach improves machine reliability, reduces downtime, enhances maintenance efficiency,
and supports data-driven industrial operations.
