Big data: new perspective of process quality control and improvement driven by data

被引：0
|
作者：
Ren M. [1 ]
Song Y. [1 ]
机构：
[1] Key Laboratory of Process Optimization and Intelligent Decision Making, Ministry of Education, Hefei University of Technology, Hefei
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 11期
关键词：
Big data; Life cycle; Process control; Process optimization; Quality prediction;
D O I：
10.13196/j.cims.2019.11.004
中图分类号：
学科分类号：
摘要：
In manufacturing big data environment, to solve the problems of production and operation management with a new way of thinking for global, dynamic and developmental, the present research results at home and abroad were summarized aiming at data-driven process quality control and improvement, and the existing problems were analyzed. The data life cycle theory was introduced into process quality control, and a continuous evolution framework of quality control based on process data life cycle was proposed. Then the dynamic process of the collection, storage, updating and application of quality data to real-time quality control and continuous improvement was described in detail. By linking data, problems and knowledge together, the management and reuse of quality data as well as the accumulation and inheritance of quality knowledge in the production process had been explored. The further research directions were given. © 2019, Editorial Department of CIMS. All right reserved.
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页码：2731 / 2742
页数：11
相关论文
共 85 条
  • [1] Li J., Ni J., Wang A., From Big Data to Intelligent Manufacturing, pp. 1-5, (2016)
  • [2] Padhke M., Quality Engineering Using Robust Design, (1989)
  • [3] Zhuo D., Xu J., Survey of the techniques and applications on quality diagnosis, Journal of Systems Engineering, 23, 3, pp. 338-346, (2008)
  • [4] Shi J., Zhou S., Quality control and improvement for multistage systems: a survey, IIE Transactions, 41, 9, pp. 744-753, (2009)
  • [5] Ge Z., Song Z., Ding S.X., Et al., Data mining and analytics in the process industry: the role of machine learning, IEEE Access, 5, pp. 20590-20616, (2017)
  • [6] Yin S., Ding S.X., Xie X., Et al., A review on basic data-driven approaches for industrial process monitoring, IEEE Transactions on Industrial Electronics, 61, 11, pp. 6418-6428, (2014)
  • [7] Peng K., Ma L., Zhang K., Review of quality-related fault detection and diagnosis techniques for complex industrial processes, Acta Automatica Sinica, 43, 3, pp. 349-365, (2017)
  • [8] Zhang X., Performance monitoring, fault diagnosis and quality prediction based on statistical theory, pp. 2-10, (2008)
  • [9] Tsung F., Statistical monitoring and diagnosis of automatic controlled process using dynamic PCA, International Journal of Production Research, 38, 3, pp. 625-637, (2000)
  • [10] Chen J.H., Liao C.M., Dynamic process fault monitoring based on neural network and PCA, Journal of Process Control, 12, 2, pp. 277-289, (2002)
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