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Title: Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Journal Article · · Journal of Manufacturing Systems
 [1];  [2];  [3];  [4];  [3];  [4];  [2]
  1. California State University, Northridge (CSUN), CA (United States); University of California, Los Angeles, CA (United States); OSTI
  2. California State University, Northridge (CSUN), CA (United States); University of California, Los Angeles, CA (United States)
  3. California State University, Northridge (CSUN), CA (United States)
  4. University of California, Irvine, CA (United States)
+ Show Author Affiliations

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.

Research Organization:
University of California, Los Angeles, CA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Aeronautics and Space Administration (NASA)
Grant/Contract Number:
EE0007613
OSTI ID:
1977354
Journal Information:
Journal of Manufacturing Systems, Journal Name: Journal of Manufacturing Systems Journal Issue: C Vol. 61; ISSN 0278-6125
Publisher:
Elsevier - Society of Manufacturing EngineersCopyright Statement
Country of Publication:
United States
Language:
English

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conference July 2017
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42 ENGINEERING
energy disaggregation
machine learning
object detection
real-time monitoring
smart connected worker
text recognition