Spatial modeling and monitoring considering long-range dependence

IF 2.6 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Journal of Quality Technology Pub Date : 2023-11-14 DOI:10.1080/00224065.2023.2260018
Yunfei Shao, Wujun Si, Yong Chen
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In literature, although various Markovian and non-Markovian spatial models have been proposed to study material surfaces, none of them has yet considered the LRD effect, which can lead to inefficient surface characterization and inaccurate surface quality control. To overcome the challenge, in this article, we first propose a novel spatial model that can capture the spatial LRD on material surfaces. Both isotropic and anisotropic scenarios of the model are developed based on the Lévy fractional Brownian random field and the fractional Brownian sheet, respectively. Subsequently, based on the proposed spatial model we develop an LRD-integrated quality control framework to monitor surface quality via generalized likelihood ratio test. Comprehensive simulation studies and a real case study using images of wood surfaces are conducted to validate the proposed approach. Results show that the proposed model that integrates LRD significantly outperforms multiple existing models in anomaly detection, and traditional models mis-detect out-of-control surfaces when the spatial LRD actually presents.Keywords: fractional Brownian sheetimage characterizationLévy fractional Brownian random fieldspatial long-range dependencesurface monitoring AcknowledgmentsThe authors would like to thank the Associate Editor and two anonymous reviewers for their thoughtful and constructive comments that significantly improved the quality of this article.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data of wood images used in the case study is publicly available at: https://www.mvtec.com/company/research/datasets/mvtec-ad.Additional informationFundingThis work was supported in part by the National Science Foundation under Award OIA-1656006, the Kansas NASA EPSCoR Research Infrastructure Development Program under Grant 80NSSC22M0028, and the NASA EPSCoR Program under Grant 80NSSC23M0100 to Wichita State University.Notes on contributorsYunfei ShaoYunfei Shao received a B.S. degree in theoretical and applied mechanics from the University of Science and Technology of China, Hefei, China, in 2016, and a Ph.D. degree in industrial engineering from Wichita State University, Wichita, KS, USA, in 2023. His research interests are in the development of statistical and data mining methods in reliability engineering and maintenance planning. He will become a post-doctoral researcher at Tsinghua University, Beijing, China, soon.Wujun SiWujun Si received a B.Eng. degree in mechanical engineering from the University of Science and Technology of China, Hefei, China, in 2013, and a Ph.D. degree in industrial engineering from Wayne State University, Detroit, MI, USA, in 2018. He is currently an Assistant Professor in the Department of Industrial, Systems, and Manufacturing Engineering at Wichita State University, Wichita, KS, USA. His work has been published in Technometrics, Journal of Quality Technology, IISE Transactions, Computers & Operations Research, and IEEE Transactions on Reliability, among others. His research interests include engineering statistics and artificial intelligence for reliability analysis and quality control of complex systems.Yong ChenYong Chen received a B.E. degree in computer science from Tsinghua University, Beijing, China, in 1998, and an M.S. degree in statistics and a Ph.D. degree in industrial and operations engineering from the University of Michigan, Ann Arbor, MI, USA, in 2003. He is currently a Professor at the Department of Industrial and Systems Engineering, The University of Iowa, Iowa City, IA, USA. His research interests include reliability modeling, robust sensor data processing, and maintenance decision making. 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Abstract

AbstractSpatial modeling and monitoring are critical in geometric characterization and quality control of material/product surfaces. With advances in metrology technology, a long-range dependence (LRD) effect has recently been detected in spatial data over different fields. The spatial LRD refers to a type of dependence that decays slowly over the distance with heavy tails and non-summable autocovariances so that the correlation is high among surface measurements across long spatial distances. Physically, the spatial LRD effect can be caused by specific spatial patterns such as certain material textures, surface profiles, or manufacturing defects. In literature, although various Markovian and non-Markovian spatial models have been proposed to study material surfaces, none of them has yet considered the LRD effect, which can lead to inefficient surface characterization and inaccurate surface quality control. To overcome the challenge, in this article, we first propose a novel spatial model that can capture the spatial LRD on material surfaces. Both isotropic and anisotropic scenarios of the model are developed based on the Lévy fractional Brownian random field and the fractional Brownian sheet, respectively. Subsequently, based on the proposed spatial model we develop an LRD-integrated quality control framework to monitor surface quality via generalized likelihood ratio test. Comprehensive simulation studies and a real case study using images of wood surfaces are conducted to validate the proposed approach. Results show that the proposed model that integrates LRD significantly outperforms multiple existing models in anomaly detection, and traditional models mis-detect out-of-control surfaces when the spatial LRD actually presents.Keywords: fractional Brownian sheetimage characterizationLévy fractional Brownian random fieldspatial long-range dependencesurface monitoring AcknowledgmentsThe authors would like to thank the Associate Editor and two anonymous reviewers for their thoughtful and constructive comments that significantly improved the quality of this article.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data of wood images used in the case study is publicly available at: https://www.mvtec.com/company/research/datasets/mvtec-ad.Additional informationFundingThis work was supported in part by the National Science Foundation under Award OIA-1656006, the Kansas NASA EPSCoR Research Infrastructure Development Program under Grant 80NSSC22M0028, and the NASA EPSCoR Program under Grant 80NSSC23M0100 to Wichita State University.Notes on contributorsYunfei ShaoYunfei Shao received a B.S. degree in theoretical and applied mechanics from the University of Science and Technology of China, Hefei, China, in 2016, and a Ph.D. degree in industrial engineering from Wichita State University, Wichita, KS, USA, in 2023. His research interests are in the development of statistical and data mining methods in reliability engineering and maintenance planning. He will become a post-doctoral researcher at Tsinghua University, Beijing, China, soon.Wujun SiWujun Si received a B.Eng. degree in mechanical engineering from the University of Science and Technology of China, Hefei, China, in 2013, and a Ph.D. degree in industrial engineering from Wayne State University, Detroit, MI, USA, in 2018. He is currently an Assistant Professor in the Department of Industrial, Systems, and Manufacturing Engineering at Wichita State University, Wichita, KS, USA. His work has been published in Technometrics, Journal of Quality Technology, IISE Transactions, Computers & Operations Research, and IEEE Transactions on Reliability, among others. His research interests include engineering statistics and artificial intelligence for reliability analysis and quality control of complex systems.Yong ChenYong Chen received a B.E. degree in computer science from Tsinghua University, Beijing, China, in 1998, and an M.S. degree in statistics and a Ph.D. degree in industrial and operations engineering from the University of Michigan, Ann Arbor, MI, USA, in 2003. He is currently a Professor at the Department of Industrial and Systems Engineering, The University of Iowa, Iowa City, IA, USA. His research interests include reliability modeling, robust sensor data processing, and maintenance decision making. Prof. Chen received the Best Paper Awards from the IIE Transactions in 2004 and 2010.
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考虑远程依赖的空间建模和监测
摘要空间建模和监测是材料/产品表面几何表征和质量控制的关键。随着计量技术的进步,近年来在不同领域的空间数据中发现了一种远程依赖效应。空间LRD是指一种随距离缓慢衰减的依赖性,具有重尾和不可求和的自协方差,因此在长空间距离的表面测量之间的相关性很高。物理上,空间LRD效应可以由特定的空间模式引起,例如某些材料纹理、表面轮廓或制造缺陷。在文献中,虽然已经提出了各种马尔可夫和非马尔可夫空间模型来研究材料表面,但它们都没有考虑LRD效应,这可能导致表面表征效率低下和表面质量控制不准确。为了克服这一挑战,在本文中,我们首先提出了一种新的空间模型,可以捕获材料表面的空间LRD。模型的各向同性和各向异性情景分别基于l分数布朗随机场和分数布朗随机场。随后,基于提出的空间模型,我们开发了一个lrd集成的质量控制框架,通过广义似然比检验来监测地表质量。利用木材表面图像进行了全面的仿真研究和实际案例研究,以验证所提出的方法。结果表明,集成LRD的模型在异常检测方面明显优于现有的多种模型,而传统模型在空间LRD实际存在时对失控面存在错误检测。关键字:分数布朗表图像表征l - -布朗随机场-空间-远程依赖-表面监测致谢作者要感谢副主编和两位匿名审稿人的周到和建设性的意见,他们的意见大大提高了本文的质量。披露声明作者未报告潜在的利益冲突。数据可用性声明案例研究中使用的木材图像数据可在以下网站公开获取:https://www.mvtec.com/company/research/datasets/mvtec-ad.Additional信息资助本工作得到了美国国家科学基金会(OIA-1656006)、堪萨斯州NASA EPSCoR研究基础设施发展计划(80NSSC22M0028)和威奇托州立大学NASA EPSCoR计划(80NSSC23M0100)的部分支持。邵云飞,2016年毕业于中国科学技术大学理论与应用力学专业,获学士学位;2023年毕业于美国威奇托州立大学工业工程专业,获博士学位。他的研究兴趣是可靠性工程和维护计划中统计和数据挖掘方法的发展。他即将成为中国北京清华大学的博士后研究员。Wujun SiWujun Si, 2013年获得中国科学技术大学机械工程学士学位,2018年获得美国密歇根州底特律韦恩州立大学工业工程博士学位。他目前是美国威奇托州立大学工业、系统和制造工程系的助理教授。他的研究成果发表在technomeics, Journal of Quality Technology, IISE Transactions, Computers & Operations Research和IEEE Transactions on Reliability等期刊上。他的研究兴趣包括工程统计和复杂系统可靠性分析和质量控制的人工智能。陈勇,1998年毕业于中国清华大学计算机科学学士学位,2003年毕业于美国密歇根大学安娜堡分校,获统计学硕士学位和工业与运营工程博士学位。他目前是美国爱荷华州爱荷华市爱荷华大学工业与系统工程系的教授。他的研究兴趣包括可靠性建模、鲁棒传感器数据处理和维护决策。陈教授于2004年和2010年分别获得IIE Transactions颁发的最佳论文奖。
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来源期刊
Journal of Quality Technology
Journal of Quality Technology 管理科学-工程:工业
CiteScore
5.20
自引率
4.00%
发文量
23
审稿时长
>12 weeks
期刊介绍: The objective of Journal of Quality Technology is to contribute to the technical advancement of the field of quality technology by publishing papers that emphasize the practical applicability of new techniques, instructive examples of the operation of existing techniques and results of historical researches. Expository, review, and tutorial papers are also acceptable if they are written in a style suitable for practicing engineers. Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days
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