Predictive Maintenance and Monitoring of Industrial Compressors Using Machine Learning: A Proactive Approach

Abstract

In the era of Industry 4.0, predictive maintenance has become a cornerstone for ensuring operational efficiency, minimizing downtime, and extending the lifespan of industrial equipment. This paper presents a comprehensive approach to predictive maintenance and real-time monitoring of industrial air compressors using machine learning techniques integrated with Internet of Things (IoT) infrastructure. The proposed framework leverages a multi-sensor setup to continuously collect critical parameters such as temperature, pressure, and flow rate from compressor units. These data streams are transmitted to a cloud-based Structured Query Language (SQL) database, enabling centralized and scalable storage for real-time analytics. A Linear Regression algorithm was trained on historical sensor data to detect performance anomalies and forecast potential failures. The optimized model was then deployed for real-time inference. When monitored parameters exceeded pre-set thresholds, the system autonomously triggered alerts through email notifications, allowing timely intervention and preventive action. The machine learning model demonstrated high reliability, achieving a prediction accuracy of 98% as measured by the Mean Squared Error (MSE) metric. The integration of IoT and machine learning facilitates proactive maintenance strategies, reducing the risk of unexpected equipment failure and enabling continuous condition monitoring without manual intervention. The findings underscore the potential of intelligent maintenance systems to drive significant improvements in asset management, cost efficiency, and operational safety across industrial settings.