期刊
COMPUTERS IN INDUSTRY
卷 137, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.compind.2021.103591
关键词
Predictive maintenance; Zero-defect manufacturing; Quality management; Artificial intelligence; Predictive control; Big data; Industrial IoT; Configurable analytics
资金
- H2020 PROPHESY project [766994]
- European Commission (EC)
- H2020 QU4LITY project [825030]
- EC
- EU
- H2020 Societal Challenges Programme [766994] Funding Source: H2020 Societal Challenges Programme
This paper presents an end-to-end platform that addresses challenges in smart manufacturing, such as integrating data sources, applying machine learning techniques, and implementing configurable digital twins. The platform has been successfully deployed and positively evaluated in various industrial settings.
Predictive maintenance, quality management, and zero-defect manufacturing are among the most prominent smart manufacturing use cases in the Industry4.0 era. Nevertheless, the development of such systems is still challenging because of the need to integrate multiple fragmented data sources, to apply advanced machine learning techniques for multi-objective optimizations, and to implement configurable digital twins that can flexibly adapt to changing industrial configurations. This paper presents the architecture, design, practical implementation, and evaluation of an end-to-end platform that addresses these challenges. The platform provides the means for collecting, managing, and routing data streams from heterogeneous cyber physical production systems, in configurable and interoperable ways. Moreover, it supports advanced data analytics by means of a novel machine learning framework that leverages quantitative rule mining. The presented platform has been successfully deployed in various industrial settings and has been positively evaluated in terms of its ability to accelerate application development, reduce unscheduled downtimes, provide increased Overall Equipment Efficiency (OEE), compute production process parameter configurations that lower the percentage of product defects, and predict product defects before they occur. (C) 2021 Published by Elsevier B.V.
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