Principal component regression-based control charts for monitoring count data

Filho, Danilo Marcondes; Sant’Anna, Ângelo Márcio Oliveira

doi:10.1007/s00170-015-8054-6

Cited by 28 publications

(3 citation statements)

References 27 publications

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“…Once a change of the residuals process is detected, it is concluded that the process itself has been changed. Marcondes and Sant'Anna (2016) proposed model-based control charts based on Poisson regression model for count defect products with multicollinearity between input variables in manufacturing processes. Park et al (2018) extended the process monitoring schemes previously proposed for monitoring count data with under and overdispersion.…”

Section: Model-based Control Chartsmentioning

confidence: 99%

Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing

Acosta

Sant’Anna

2022

IJQRM

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PurposeProcess monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing.Design/methodology/approachA new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation.FindingsThe authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models.Originality/valueThis research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

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Section: Model-based Control Chartsmentioning

confidence: 99%

Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing

Acosta

Sant’Anna

2022

IJQRM

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“…We are grateful to Dr. Salah Mahdy Mohamed, who works as an assistant Professor at Cairo University, Institute of Statistical Studies and Research(ISSR), for his helping to have the paper of Filho1, D. and Sant' Anna, A. (2016), which contains the data of our paper.…”

Section: Acknowledgementmentioning

confidence: 99%

Robust Fitting for the Number of Defects as a Measure of Product Quality

السعيد

2018

التجارة والتمويل

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“…Mahmood et al 41 proposed GLM‐based PM and DPM strategies to monitor Poisson processes. A diverse review on GLM‐based control charts can be available in Amiri et al, 42 Kuo and Chiu, 43 Maleki et al, 44 Mammadova and Özkale, 45 Marcondes Filho and Sant'Anna, 46 and Wen et al 47 The above‐mentioned model‐based schemes are developed under Shewhart, EWMA, CUSUM, PM, and DPM charting structures. Getting motivation from Asgari et al, 30 Alevizakos et al, 17 and Mahmood et al, 41 this study provides improved MA and DMA structures based on standardized residuals of Poisson regression model (i.e., SR‐MA and SR‐DMA).…”

Section: Introductionmentioning

confidence: 99%

On the improved generalized linear model‐based monitoring methods for Poisson distributed processes

Iqbal¹,

Mahmood

Nazir

et al. 2022

Concurrency and Computation

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Control charts are widely used tool that provides quality inspectors with sensitive information for maintaining manufacturing process productivity. Numerous model‐based techniques have been presented in the literature to monitor industrial operations that focus on the normal response variable. However, non‐normal response results can occur as a result of quality control operations. In such cases, a new approach based on generalized linear model that provides multiple distribution options for response variables is required to achieve better results. Therefore, this study proposes GLM‐based moving average (MA) and double moving average (DMA) schemes formed on standardized residuals derived from a fitted Poisson regression model. The productivity of suggested methods and the existing exponentially weighted moving average (EWMA) scheme is explored in terms of run length attributes. The simulation outcomes revealed that moving average schemes based on standardized residuals (i.e., SR‐MA and SR‐DMA) outperform their predecessor (i.e., SR‐EWMA). Moreover, the SR‐DMA chart, with small values of span w, has proven to be more effective at detecting minor to moderate shifts in the process mean. Finally, a case study of a 3D manufacturing operation is shown to emphasize the importance of the proposed approaches.

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Principal component regression-based control charts for monitoring count data

Cited by 28 publications

References 27 publications

Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing

Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing

Robust Fitting for the Number of Defects as a Measure of Product Quality

On the improved generalized linear model‐based monitoring methods for Poisson distributed processes

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