RECOGNITION AND ANALYSIS OF CYCLIC AND SYSTEMATIC PATTERNS IN THE PROCESS CONTROL CHARTS

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LESANY S.A.; FATEMI GHOMI S.M.T.

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Journal Paper

Paper Information

Journal: INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING) Year:2020 | Volume:35-1 | Issue:2/1 Page(s): 167-180

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Information Journal Paper

Title

RECOGNITION AND ANALYSIS OF CYCLIC AND SYSTEMATIC PATTERNS IN THE PROCESS CONTROL CHARTS

Author(s)

LESANY S.A. | FATEMI GHOMI S.M.T. | Issue Writer Certificate

Keywords

Process control charts

signi

cant patterns

cyclic pattern

systematic pattern

Abstract

The Process control charts are most important tool of statistical process control (SPC) approach. The general control procedures of these charts only monitor charts' samples individually and do not consider the obtained common information from successive samples as probable potential disorders. The existence of natural variations in the control charts is inevitable, but the appearances of signi, cant patterns in these charts warn the special disturbances in production processes and associate out-of-control situations. The natural variations often divert signi, cant patterns from their expected forms,therefore, increase of qualitative sensitivities level for study of unnatural patterns in the control charts is mandatory. In resent years, to recognize and analyze non-random patterns in the Process control charts, numerous models have been presented. These models usually cannot alarm the occurrences of various formations modes of cyclic and systematic patterns, since the periodic patterns have phase di, erence in their starting point and most of these researches merely have simulated one simple phase of their formation. On the other hand, few developer models of periodic patterns generating functions have applied the arti, cial neural networks as recognition tool. Although the neural networks are capable in patterns learning, however they have di, cult architectures, time-consuming algorithms and uncertain reliability when the sensitivities of processes to the appearances of signi, cant patterns are high. This paper introduces a new model based on , tted cosine curve of samples for more accurate discrimination of the various formations phases of cyclic and systematic patterns and more precise estimation of their corresponding parameters at di, erent levels of sensitivity. Our proposed model compares all periodic alternatives, then selects the best , tted cosine curve of samples, and , nally determines situation of process. The results of simulated tests indicate that the proposed model reduces the misclassi, cation error of cyclic and systematic patterns and decreases the estimation error average of their corresponding parameters, in comparison with developer models of periodic patterns, for the various emergences states.

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APA: Copy

LESANY, S.A., & FATEMI GHOMI, S.M.T.. (2020). RECOGNITION AND ANALYSIS OF CYCLIC AND SYSTEMATIC PATTERNS IN THE PROCESS CONTROL CHARTS. INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING), 35-1(2/1 ), 167-180. SID. https://sid.ir/paper/992546/en

Vancouver: Copy

LESANY S.A., FATEMI GHOMI S.M.T.. RECOGNITION AND ANALYSIS OF CYCLIC AND SYSTEMATIC PATTERNS IN THE PROCESS CONTROL CHARTS. INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING)[Internet]. 2020;35-1(2/1 ):167-180. Available from: https://sid.ir/paper/992546/en

IEEE: Copy

S.A. LESANY, and S.M.T. FATEMI GHOMI, “RECOGNITION AND ANALYSIS OF CYCLIC AND SYSTEMATIC PATTERNS IN THE PROCESS CONTROL CHARTS,” INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING), vol. 35-1, no. 2/1 , pp. 167–180, 2020, [Online]. Available: https://sid.ir/paper/992546/en

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