Condition-Based Monitoring for Industrial Control Panel

Reliability of industrial control panels is critical to ensure smooth running of operations in process and manufacturing industries. Conventional preventive maintenance, despite wide usage, often ignores the real-time measurements of the health of the equipment, leading either to redundant interventions or unexpected breakdowns. This work presents a Condition-Based Monitoring (CBM) system specifically designed on industrial control panels, and such a system integrates multi-sensor data gathering, predictive modeling, and decision-making aids. It employs IoT-facilitated sensors to monitor thermal, electrical, environmental, vibration, and gas parameters, along with machine learning algorithms to detect anomlies and estimate Remaining Useful Life (RUL). A three-month pilot at a manufacturing plant demonstrated the effectiveness of the system, achieving a fault detection accuracy level of 93.5%, RUL estimation accuracy level of 89%, along with significant reduction in unplanned downtime by as much as 28%, and maintenance expenses by as much as 22%. Findings confirm that CBM significantly enhanced the level of operational reliability and cost-effectiveness compared to the conventional approaches of preventive maintenance.