Control chart pattern recognition for imbalanced data based on multi-feature fusion using convolutional neural network

Li, Xue; Wu, Haochen; Zheng, Huaping; He, Zhen

doi:10.1016/j.cie.2023.109410

Cited by 16 publications

(3 citation statements)

References 37 publications

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“…Zhang et al 7 , Yu and Liu 8 , Maged et al 9 , and Yu et al 10 , 11 achieved excellent performance in failure detection in high-dimensional or complex processes using deep learning methods. Moreover, Zan et al 12 , Lu et al 13 , Yu and Zhang 14 , Lee et al 15 , and Xue et al 16 used machine and deep learning methods to recognize abnormal control chart patterns.…”

Section: Introductionmentioning

confidence: 99%

Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks

Lee,

Liao

2024

Sci Rep

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In recent years, deep learning methods have been widely used in combination with control charts to improve the monitoring efficiency of complete data. However, due to time and cost constraints, data obtained from reliability life tests are often type-I right censored. Traditional control charts become inefficient for monitoring this type of data. Thus, researchers have proposed various control charts with conditional expected values (CEV) or conditional median (CM) to improve efficiency for right-censored data under normal and non-normal conditions. This study combines the exponentially weighted moving average (EWMA) CEV and CM chart with deep learning methods to increase efficiency for gamma type-I right-censored data. A statistical simulation and a real-world case are presented to assess the proposed method, which outperforms the traditional EWMA charts with CEV and CM in various skewness coefficient values and censoring rates for gamma type-I right-censored data.

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Section: Introductionmentioning

confidence: 99%

Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks

Lee,

Liao

2024

Sci Rep

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“…In recent years, deep neural networks have been applied to solve pattern recognition, function fitting, etc. Xue et al [13] applied the convolutional neural network to pattern recognition. Belomestny et al [14] applied deep neural networks to approximate the nonlinear function and its derivatives simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of Ship Roll Motion in Waves Based on Convolutional Neural Network

Hou,

Xia

2024

JMSE

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In this study, a short-term prediction method for ship roll motion in waves based on convolutional neural network (CNN) is presented. Firstly, based on the ship roll motion equation, the data for free roll attenuation motion in still water, roll motion in regular waves, and roll motion excited by irregular waves are simulated, respectively. Secondly, the simulation data is normalized and preprocessed, and then the time-sliding window technique is applied to construct the training and testing sample sets. Thirdly, the CNN model is trained by learning from the constructed training sample sets, and the well-trained CNN model is applied to predict the roll motion. To validate the CNN model’s prediction accuracy and effectiveness, a comparison between the forecasted results and the simulation data is conducted. Meanwhile, the predicted results are also compared with that of the long-short-term memory (LSTM) neural network. The research results demonstrate that CNN can effectively achieve accurate prediction of ship roll motion in waves, and its prediction accuracy is the same as that of the LSTM neural network.

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“…Deep-learning methods have achieved excellent performance in failure detection in high-dimensional or complex processes [3,[26][27][28]. Moreover, some researchers recognized abnormal control chart patterns using machine-or deep-learning methods [29][30][31][32][33][34]. However, to the authors' knowledge, there have been no relevant studies that use deeplearning or machine-learning methods to monitor right-censored data.…”

Section: Introductionmentioning

confidence: 99%

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

Lee,

Liao

2023

Mathematics

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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.

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Control chart pattern recognition for imbalanced data based on multi-feature fusion using convolutional neural network

Cited by 16 publications

References 37 publications

Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks

Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks

Short-Term Prediction of Ship Roll Motion in Waves Based on Convolutional Neural Network

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

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