Machine Condition Monitoring of Production Equipment

Sammanfattning: This study investigates the possibility of implementing a machine monitoring system on IBAS2, a production machine responsible for aligning an optical lens with an image sensor. A machine-monitoring system could possibly reduce downtime, costs, and production recalls. After examining IBAS2, vibration analysis emerged as a promising monitoring approach. The research aimed to capture the natural vibrations exhibited by the machine during normal operation, serving as a baseline for understanding its functioning under normal conditions. The results obtained from this investigation demonstrate repetitive vibration patterns associated with specific machine components. Moreover, altering the velocity of a machine component leads to a distinct variation in the vibration pattern observed from the collected data. Furthermore, the results obtained from vibration measurements exhibit promising potential for detecting indications of machine wear. By leveraging accurate data that establish the machine’s normal vibration patterns, we propose a future implementation of an AI model designed to detect deviations from the norm. This could lead to a vibration-focused machine monitoring system that predicts upcoming failures in IBAS2.