{"title":"利用支持向量回归的自适应 EWMA 控制图","authors":"Muhammad Waqas Kazmi, Muhammad Noor‐ul‐Amin","doi":"10.1002/qre.3603","DOIUrl":null,"url":null,"abstract":"Traditional control charts depend on the process parameters that are used to monitor the shifts in the process. The adaptive control charts are used to adapt a process parameter during the online monitoring. This research introduces a support vector regression (SVR) based adaptive exponentially weighted moving average control chat to enhance the monitoring of the mean in industrial processes. The study systematically investigates the comparative efficiency of linear, radial basis function (RBF), and polynomial functions within the SVR framework. The proposed SVR‐based AEWMA control chart leverages the strengths of the RBF kernel, providing a robust mechanism for detecting shifts in the process mean by adapting the smoothing constant according to the size of the shift. To validate the efficacy of the proposed methodology, a practical application is presented by using real‐life data. The application showcases the adaptability and reliability of the SVR‐based adaptive EWMA control chart in effectively monitoring location shifts.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive EWMA control chart by using support vector regression\",\"authors\":\"Muhammad Waqas Kazmi, Muhammad Noor‐ul‐Amin\",\"doi\":\"10.1002/qre.3603\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional control charts depend on the process parameters that are used to monitor the shifts in the process. The adaptive control charts are used to adapt a process parameter during the online monitoring. This research introduces a support vector regression (SVR) based adaptive exponentially weighted moving average control chat to enhance the monitoring of the mean in industrial processes. The study systematically investigates the comparative efficiency of linear, radial basis function (RBF), and polynomial functions within the SVR framework. The proposed SVR‐based AEWMA control chart leverages the strengths of the RBF kernel, providing a robust mechanism for detecting shifts in the process mean by adapting the smoothing constant according to the size of the shift. To validate the efficacy of the proposed methodology, a practical application is presented by using real‐life data. The application showcases the adaptability and reliability of the SVR‐based adaptive EWMA control chart in effectively monitoring location shifts.\",\"PeriodicalId\":56088,\"journal\":{\"name\":\"Quality and Reliability Engineering International\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quality and Reliability Engineering International\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1002/qre.3603\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality and Reliability Engineering International","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/qre.3603","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Adaptive EWMA control chart by using support vector regression
Traditional control charts depend on the process parameters that are used to monitor the shifts in the process. The adaptive control charts are used to adapt a process parameter during the online monitoring. This research introduces a support vector regression (SVR) based adaptive exponentially weighted moving average control chat to enhance the monitoring of the mean in industrial processes. The study systematically investigates the comparative efficiency of linear, radial basis function (RBF), and polynomial functions within the SVR framework. The proposed SVR‐based AEWMA control chart leverages the strengths of the RBF kernel, providing a robust mechanism for detecting shifts in the process mean by adapting the smoothing constant according to the size of the shift. To validate the efficacy of the proposed methodology, a practical application is presented by using real‐life data. The application showcases the adaptability and reliability of the SVR‐based adaptive EWMA control chart in effectively monitoring location shifts.
期刊介绍:
Quality and Reliability Engineering International is a journal devoted to practical engineering aspects of quality and reliability. A refereed technical journal published eight times per year, it covers the development and practical application of existing theoretical methods, research and industrial practices. Articles in the journal will be concerned with case studies, tutorial-type reviews and also with applications of new or well-known theory to the solution of actual quality and reliability problems in engineering.
Papers describing the use of mathematical and statistical tools to solve real life industrial problems are encouraged, provided that the emphasis is placed on practical applications and demonstrated case studies.
The scope of the journal is intended to include components, physics of failure, equipment and systems from the fields of electronic, electrical, mechanical and systems engineering. The areas of communications, aerospace, automotive, railways, shipboard equipment, control engineering and consumer products are all covered by the journal.
Quality and reliability of hardware as well as software are covered. Papers on software engineering and its impact on product quality and reliability are encouraged. The journal will also cover the management of quality and reliability in the engineering industry.
Special issues on a variety of key topics are published every year and contribute to the enhancement of Quality and Reliability Engineering International as a major reference in its field.