Statistical process monitoring as a big data analytics tool for smart manufacturing

被引：125

作者：

He, Q. Peter ^{[1
]}

Wang, Jin ^{[1
]}

机构：

[1] Auburn Univ, Dept Chem Engn, Auburn, AL 36849 USA

来源：

JOURNAL OF PROCESS CONTROL | 2018年 / 67卷

基金：

美国国家科学基金会;

关键词：

Statistical process monitoring; Big data; Smart manufacturing; Feature extraction; Internet of things; PRINCIPAL COMPONENT ANALYSIS; PROJECTION-PURSUIT APPROACH; HIGHER-ORDER STATISTICS; FAULT-DETECTION; BATCH PROCESSES; DISTURBANCE DETECTION; DISPERSION MATRICES; DYNAMIC PROCESSES; PATTERN-ANALYSIS; NEURAL-NETWORKS;

D O I：

10.1016/j.jprocont.2017.06.012

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：35 / 43

页数：9

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