Residual Control Chart Based on a Convolutional Neural Network and Support Vector Regression for Type-I Censored Data with the Weibull Model

Pei-Hsi Lee () and Shih-Lung Liao
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Pei-Hsi Lee: Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Shih-Lung Liao: Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan

Mathematics, 2023, vol. 12, issue 1, 1-14

Abstract: Control charts with conditional expected value (CEV) can be used with novel statistical techniques to monitor the means of moderately and lowly censored data. In recent years, machine learning and deep learning have been successfully combined with quality technology to solve many process control problems. This paper proposes a residual control chart combining a convolutional neural network (CNN) and support vector regression (SVR) for type-I censored data with the Weibull model. The CEV and exponentially weighted moving average (EWMA) statistics are used to generate training data for the CNN and SVR. The average run length shows that the proposed chart approach outperforms the traditional EWMA CEV chart approach in various shift sizes and censored rates. The proposed chart approach is suitable to be used in detecting small shift size for highly censored data. An illustrative example presents the application of the proposed method in an electronics industry.

Keywords: convolutional neural network; support vector regression; type-I right censored data; Weibull model; conditional expected value; residual control chart (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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