Development of Machine Down-Time Monitoring System for Production Line Efficiency Evaluation

Manufacturing industries often face challenges in optimizing machine performance due to the limitations of traditional downtime monitoring methods, which are time-consuming and prone to errors. The lack of real-time capabilities in these methods leads to delayed identification and resolution of machine issues, ultimately affecting productivity. This research aims to develop a real-time machine downtime monitoring system that leverages sensors, vision systems, and LabVIEW software to enhance the detection and analysis of production line performance. The system uses image processing techniques for product quality assessment, enabling the detection of good and defective products, and integrates vibration sensors to monitor equipment conditions. The Arduino microcontroller is employed to manage sensor data and motor functions, while LabVIEW software facilitates real-time visualization and data analysis. The system demonstrated high accuracy in detecting both product defects and equipment vibrations, although sensitivity to lighting conditions and low-powered motors presents areas for future improvement. The integration of this system into production lines has the potential to significantly reduce downtime and improve operational efficiency, contributing to more automated and reliable industrial processes.