Integration of machine learning techniques and control charts in multivariate processes

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Demircioglu Diren d.; Boran s.; Cil i.

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Journal Paper

Paper Information

Journal: Scientia Iranica Year:2020 | Volume:27 | Issue:6 (Transactions E: Industrial Engineering) Page(s): 3233-3241

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Information Journal Paper

Title

Integration of machine learning techniques and control charts in multivariate processes

Author(s)

Demircioglu Diren d. | Boran s. | Cil i. | Issue Writer Certificate

Keywords

Multivariate control chart

Naive Bayes-kernel

K-nearest neighbor

Decision tree

Artificial neural networks

Multi-layer perceptron

Deep learning

Abstract

Using Multivariate control chart instead of univariate control chart for all variables in processes provides more time and labor advantages that are of significance in the relations among variables. However, the statistical calculation of the measured values for all variables is regarded as a single value in the control chart. Therefore, it is necessary to determine which variable(s) are the cause of the out-of-control signal. Effective corrective measures can only be developed when the causes of the fault(s) are correctly determined. The present study was aimed at determining the machine learning techniques that could accurately estimate the fault types. Through the Hotelling T2 chart, out-of control signals were identified and the types of faults affected by the variables were specified. Various machine learning techniques were used to compare classification performances. The developed model was employed in the evaluation of paint quality in a painting process. Artificial neural networks (ANNs) was determined as the most successful technique in terms of the performance criteria. The novelty of this study lies in its classification of the faults according to their types instead of those of the variables. Defining the faults based on their types facilitates taking effective and corrective measures when needed.

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APA: Copy

Demircioglu Diren, d., Boran, s., & Cil, i.. (2020). Integration of machine learning techniques and control charts in multivariate processes. SCIENTIA IRANICA, 27(6 (Transactions E: Industrial Engineering)), 3233-3241. SID. https://sid.ir/paper/973898/en

Vancouver: Copy

Demircioglu Diren d., Boran s., Cil i.. Integration of machine learning techniques and control charts in multivariate processes. SCIENTIA IRANICA[Internet]. 2020;27(6 (Transactions E: Industrial Engineering)):3233-3241. Available from: https://sid.ir/paper/973898/en

IEEE: Copy

d. Demircioglu Diren, s. Boran, and i. Cil, “Integration of machine learning techniques and control charts in multivariate processes,” SCIENTIA IRANICA, vol. 27, no. 6 (Transactions E: Industrial Engineering), pp. 3233–3241, 2020, [Online]. Available: https://sid.ir/paper/973898/en

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