Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory

Abstract

As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In previous studies, a machine, conducting a single continuous operation, has been mainly observed for power estimation. However, a modularized production line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of such production lines, it is important to interpret and distinguish mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline from data collection from different sources to pre-processing, data conversion, synchronization, and deep learning classification to estimate the total power use of the future process plan is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building power estimation models without manual data pre-processing. The proposed system is applied to a modular factory connected with machine controllers using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline with the result of the power profile synchronized with the robot program.