Statistical process monitoring as a big data analytics tool for smart manufacturing. (July 2018)

• Record Type: Journal Article
• Authors: He, Q. Peter; Wang, Jin
• Is Part Of: Journal of process control. Volume 67(2018)
• Page Start: 35
• Page End: 43
• Publication Date: 2018-07
• Subjects: Statistical process monitoring -- Big data -- Smart manufacturing -- Feature extraction -- Internet of things; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.06.012
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A roadmap of statistical process monitoring (SPM) is proposed and most recent developments are reviewed and summarized. Challenges and potential solutions to manufacturing big data are discussed and feature based SPM is suggested as promising. The future directions of SPM and advantages of feature based SPM are discussed in the context of smart manufacturing. Abstract: With ever-accelerating advancement of information, communication, sensing and characterization technologies, such as industrial Internet of Things (IoT) and high-throughput instruments, it is expected that the data generated from manufacturing will grow exponentially, generating so called 'big data'. One of the focuses of smart manufacturing is to create manufacturing intelligence from real-time data to support accurate and timely decision-making. Therefore, big data analytics is expected to contribute significantly to the advancement of smart manufacturing. In this work, a roadmap of statistical process monitoring (SPM) is presented. Most recent developments in SPM are briefly reviewed and summarized. Specific challenges and potential solutions in handling manufacturing big data are discussed. We suggest that process characteristics or feature based SPM, instead of process variable based SPM, is a promising route for next generation SPM and could play a significant role in smart manufacturing. The advantages of feature based SPM are discussed to support the suggestion and future directions in SPM are discussed in the context of smart manufacturing.