Development and Implementation of an Industrial IoT-Based Real-Time Process Monitoring System to Improve Overall Equipment Effectiveness (O.E.E.) of Bearing Manufacturing Process

Abstract

The research study presents the design and implementation of an IoT-based real-time monitoring system to enhance Overall Equipment Effectiveness (O.E.E) of External grinding process used in precision bearing manufacturing. The developed system continuously monitors critical parameters such as cutting temperature, machine vibration, noise and cycle time using calibrated sensors. These critical parameters are directly linked to Key Performance Indicators (KPIs) of O.E.E in terms of Availability, Quality and Performance. A systematic layered IoT architecture is developed to enable real-time data acquisition, cloud storage, visualization and alert generation through a customized web application. Controlled experiments were conducted on various batches of bearing races under fixed machining conditions with critical parameter thresholds defined based on ISO standards and historical production data. The system successfully identified deviations, enabling timely corrective actions and reducing unexpected breakdowns. The real-time dashboard and alert system provided actionable insights, improving operational decision-making and minimizing manual documentation. As a result, OEE improved from 86% to 94.70%, demonstrating an 8.70% increase. Additionally, reductions in breakdowns, defects, and downtime were observed. This study confirms that integrating IoT in manufacturing significantly enhances equipment performance and product quality through data-driven process control.