One‐sided control chart based on support vector machines with differential evolution algorithm

Wang, Fu‐Kwun; Bizuneh, Berihun; Cheng, Xiao‐Bin

doi:10.1002/qre.2465

Cited by 16 publications

(8 citation statements)

References 29 publications

(45 reference statements)

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“…51 These are only a few topics for future research in this area. A first step was taken here in controlling by its upper and lower control limits to ensure the economic viability of a process and quality of service on the user side.…”

Section: Discussionmentioning

confidence: 99%

“…Another line of research includes considering variations in the arrival rate and a search for control strategies via automatic adjustment of the number of servers s. Support vector machines represent another emerging technique that has begun to bear fruit in the area of control charts. 51 These are only a few topics for future research in this area.…”

Section: Discussionmentioning

confidence: 99%

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Control charts for traffic intensity monitoring of Markovian multiserver queues

Cruz

Quinino

2019

Quality & Reliability Eng

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A number of recent research studies have applied queueing theory as an approximate modeling tool to mathematically describe industrial systems, which include manufacturing, distribution, and service, for instance. Among the main observable characteristics in queues, the number of users in the system can be controlled to keep waiting times as minimal as possible. The design of efficient control charts is an attempt to monitor and control such systems. Control charts are proposed to monitor infinite queues with Markovian arrivals, exponential service times, and s identical parallel servers. The proposed charts monitor traffic intensities, which are the ratio between the arrival rate and the service rate, estimated through the number of users in the queueing system at random epochs. The effectiveness and efficiency of the proposed approaches in terms of the average run lengths are established by a comprehensive set of Monte Carlo simulations. KEYWORDS attribute control charts, average run length, Markovian queues, quality control wileyonlinelibrary.com/journal/qre [Colour figure can be viewed at wileyonlinelibrary.com] [Colour figure can be viewed at wileyonlinelibrary.com] Frederico R. B. Cruz holds a bachelor's degree in electrical engineering (1988) as well as a master's degree (1991) and a doctorate (1997) in computer science from the Universidade Federal de Minas Gerais, where he is a full professor in the Department of Statistics and conducts research in operations research.Roberto C. Quinino has a PhD in production engineering from the Universidade de São Paulo (1998). He is currently a full professor at the Universidade Federal de Minas Gerais. He develops research on quality control and marketing engineering with an emphasis on stochastic modeling applications acting on the following subjects: Taguchi online, Markov chains, misclassification, optimization, control chart planning, regression analysis, planning of experiments, and conjoint analysis. He supervises undergraduate, specialization, master's, and doctoral students. He has published papers in several international journals.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Control charts for traffic intensity monitoring of Markovian multiserver queues

Cruz

Quinino

2019

Quality & Reliability Eng

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“…Zhang et al 48 developed a general monitoring framework for detecting location shifts in complex processes using the SVM model and multivariate EWMA chart. Later, Wang et al 49 developed SVM-based one-sided control charts to monitor a process with multivariate quality characteristics. They used the differential evolution (DE) algorithm to obtain the optimal parameters of the SVM model by minimizing mean absolute error.…”

Section: Kernel-based Learning Methodsmentioning

confidence: 99%

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Tran¹,

Nadi

Nguyễn

et al. 2021

Springer Series in Reliability Engineering

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Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things and Artificial Intelligence (AI), Smart Manufacturing (SM) has become an important concept for expressing the end goal of digitization in manufacturing. However, SM requires a more automatic procedure with capabilities to deal with huge data from the continuous and simultaneous process. Hence, traditional control charts of SPC now find difficulties in reality activities including designing, pattern recognition, and interpreting stages. Machine Learning (ML) algorithms have emerged as powerful analytic tools and great assistance that can be integrating to control charts of SPC to solve these issues. Therefore, the purpose of this chapter is first to presents a survey on the applications of ML techniques in the stages of designing, pattern recognition, and interpreting of control charts respectively in SPC especially in the context of SM for AD. Second, difficulties and challenges in these areas are discussed. Third, perspectives of ML techniquesbased control charts for AD in SM are proposed. Finally, a case study of an ML-based control chart for bearing failure AD is also provided in this chapter.

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“…In recent years, support vector machine (SVM) has achieved remarkable results in the application of statistical process control. [12][13][14][15][16] To improve the quality detection ability of products, quality determination boundary detection methods based on support vector data description (SVDD) continue to emerge. [17][18][19][20] Combining SVDD with nuclear technology, Ben et al used a method to detect the product quality of nonlinear characteristics of multimodal processes.…”

Section: Introductionmentioning

confidence: 99%

A clustering hyperrectangle model based on kernel density estimation for product quality detection

Lü

Xie

Tang

2022

Quality & Reliability Eng

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With the rapid development of sensor technology, a huge amount of data is generated in the industrial manufacturing process, which poses new challenges for product quality detection. Meanwhile, the process data often have high‐dimensional characteristics of nonlinearity and strong correlation, so traditional methods are not suitable for quick quality detection. In this paper, to further enhance the speed and accuracy of quality detection, a clustering hyperrectangle model based on a kernel density estimation is proposed. First, based on the kernel density estimation, the distribution characteristics of data, which can directly provide optimal clustering parameter for the k‐means method, can be obtained. Next, to form the detection boundary of every hyperrectangle, the mean vector formed by clustering is used as the basis for determining the center of the hyperrectangle. Then, the distance between every sample point and the center of hyperrectangle is calculated for building the final detection condition. Furthermore, the joint determination based on the detection condition is used for effectively guaranteeing the detection accuracy. Finally, based on the simulation and experimental results, compared with the traditional methods, the proposed method can determine the detection condition in a more refined way, and has the merits of shorter test time and higher accuracy.

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One‐sided control chart based on support vector machines with differential evolution algorithm

Cited by 16 publications

References 29 publications

Control charts for traffic intensity monitoring of Markovian multiserver queues

Control charts for traffic intensity monitoring of Markovian multiserver queues

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

A clustering hyperrectangle model based on kernel density estimation for product quality detection

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