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

Jumyung Um, Taebyeong Park, Hae-Won Cho and Seung-Jun Shin
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Jumyung Um: Department of Industrial & Management Systems Engineering, Kyung Hee University, Yongin 17104, Korea
Taebyeong Park: Department of Industrial & Management Systems Engineering, Kyung Hee University, Yongin 17104, Korea
Hae-Won Cho: Department of Applied Systems, Hanyang University, Seoul 04763, Korea
Seung-Jun Shin: School of Interdisciplinary Industrial Studies, Hanyang University, Seoul 04763, Korea

Sustainability, 2022, vol. 14, issue 7, 1-20

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.

Keywords: modular factory; industry 4.0; smart factory; energy-efficient process; deep learning; classification; neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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