An Energy Management System (EMS) monitors, evaluates, and controls the performance of different energy-consuming equipment such as motors and compressors and extending to plant-floor machinery. This research explores and develops a systematic framework and statistically-significant analytic models for using electric consumption power variables as an indicator for machine-level health or performance. This is in an effort to explore new techniques for improving the current capabilities of traditional energy management systems.
Power data is collected real-time for electrical power consumption usage of machines, under consistent operational conditions. Three levels of performance assessment and associated models are developed based on acquired power signals that effectively consider the power consumed by a machine as an indicator for overall machine performance. The research hypothesis is that a relationship exists between a machine’s electric energy consumption levels and the machine’s level of performance and potential health degradation. An intuitive predictive model is developed to give a power-based performance prediction for one machining cycle or cycle step ahead.
The models are successfully implemented and validated on a real-world industrial case study for an injection molding process where electrical power consumption data is collected. A standard moving average method is used to benchmark the results of this analysis.