Journal of Intelligent Manufacturing

, Volume 24, Issue 6, pp 1241–1252

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Authors

    • Centre for Intelligent Systems ResearchDeakin University
  • Zhiqiang Cao
    • State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of Sciences
  • Liangjun Xie
    • Schlumberger Limited
  • Douglas Creighton
    • Centre for Intelligent Systems ResearchDeakin University
  • Min Tan
    • State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of Sciences
  • Saeid Nahavandi
    • Centre for Intelligent Systems ResearchDeakin University
Article

DOI: 10.1007/s10845-012-0659-0

Cite this article as:
Gu, N., Cao, Z., Xie, L. et al. J Intell Manuf (2013) 24: 1241. doi:10.1007/s10845-012-0659-0

Abstract

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Keywords

Control chartsConcurrent patternsSingular spectrum analysisLearning vector quantization networksAluminium smelting

Copyright information

© Springer Science+Business Media, LLC 2012