{"title":"利用 X 射线图像对制造过程监控中的异常检测方法进行比较研究","authors":"Congfang Huang, David Blondheim, Shiyu Zhou","doi":"10.1007/s10845-024-02435-x","DOIUrl":null,"url":null,"abstract":"<p>Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling <span>\\(T^2\\)</span> statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"83 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images\",\"authors\":\"Congfang Huang, David Blondheim, Shiyu Zhou\",\"doi\":\"10.1007/s10845-024-02435-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling <span>\\\\(T^2\\\\)</span> statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.</p>\",\"PeriodicalId\":16193,\"journal\":{\"name\":\"Journal of Intelligent Manufacturing\",\"volume\":\"83 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s10845-024-02435-x\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02435-x","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
制造生产系统中的过程监控和异常检测对生产操作的一致性、可靠性和质量至关重要,因此开发了大量异常检测方法。在这项工作中,对代表性的基于无监督 X 射线图像的异常检测方法进行了比较研究。研究考虑并比较了基于统计、物理和深度学习的降维方法以及不同的异常检测标准。对真实世界的 X 射线图像数据进行了模拟异常和真实异常的案例研究。以霍特林(T^2\)统计量作为检测标准的灰度共现矩阵在模拟异常案例中取得了最佳性能,总体检测准确率达到 96%。以重建误差为标准的主成分分析法在真实异常情况下的检测率最高,达到 90.6%。考虑到图像数据在智能制造过程中的可用性越来越高,这项研究将提供非常有用的知识,并会有广泛的受众。
A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images
Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling \(T^2\) statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.