Detecting Idle Energy Waste in Industrial Facilities Using IoT Data and Software-Based Analysis

Sammanfattning

Industrial manufacturing accounts for a significant share of global energy consumption. A substantial part of that energy is consumed in non productive activities such as machines in idle states, unplanned downtime and fault induced stops. This thesis investigates the potential of software based idle detection to quantify and reduce non productive energy consumption in industrial production lines. A modular analyzing prototype was developed and applied to a 30 day dataset comprising 864,710 records across two production lines. Line A is in the automotive industry and Line B is in the food industry. The prototype identifies idle periods and patterns, pre fault trends and estimates costs savings achievable through automated shutdown intervention. The results shows that both lines spends over 50% of the measured period in idle states. The both machines have combined consumed 8,231.45 kWh of non productive energy at a cost of 12,347 SEK. By targeting idle periods exceeding 30 minutes and accounting for the startup energy penalties the net savings for line A is 6,074 SEK and for line B is 6,200 SEK. This estimates a annual saving of 73,332 SEK and 74,832 SEK of the respective production line. Fault induced idle analysis identified that hydraulic leakage as the dominant source of fault related energy loss across bothlines. The findings confirm that software based monitoring is a technically feasible and economically justified approach to reducing industrial energy waste, contributing both to operational costs reduction and the broader decarbonization agenda in manufacturing.