AN INTELLIGENT PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL IOT APPLICATIONS
TL;DR
Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
Abstract
Predictive maintenance has become a key application of the Industrial Internet of Things (IIoT), helping industries improve equipment reliability and operational efficiency. Conventional maintenance approaches, such as reactive and preventive maintenance, often result in unexpected equipment failures, increased maintenance costs, and inefficient use of resources. By integrating real-time sensor monitoring with machine learning techniques, predictive maintenance enables early detection of potential faults, allowing maintenance activities to be performed only when necessary. This study presents a real-time predictive maintenance framework for Industrial IoT systems using machine learning. The proposed solution collects live sensor data, including temperature, vibration, and pressure, from industrial equipment. The data is preprocessed and analyzed using Python-based tools before being fed into machine learning models to identify anomalies and predict potential equipment failures. The system provides timely maintenance recommendations, minimizing unplanned downtime and improving overall equipment performance. Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments. Keywords—Industrial Automation, Machine Learning, Predictive Maintenance, Sensor Networks, Equipment Failure Prediction.