{"title":"基于康托洛维奇距离多块变异自动编码器和贝叶斯推理的分布式过程监控","authors":"","doi":"10.1016/j.cjche.2024.05.016","DOIUrl":null,"url":null,"abstract":"<div><p>Modern industrial processes are typically characterized by large-scale and intricate internal relationships. Therefore, the distributed modeling process monitoring method is effective. A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder (KD-MBVAE) is introduced. Firstly, given the high consistency of relevant variables within each sub-block during the change process, the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory. Subsequently, the variational autoencoder (VAE) model was separately established, and corresponding <em>T</em><sup>2</sup> statistics were calculated. To improve fault sensitivity further, a novel statistic, derived from Kantorovich distance, is introduced by analyzing model residuals from the perspective of probability distribution. The thresholds of both statistics were determined by kernel density estimation. Finally, monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference. Additionally, a novel approach for fault diagnosis is introduced. The feasibility and efficiency of the introduced scheme are verified through two cases.</p></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed process monitoring based on Kantorovich distance-multiblock variational autoencoder and Bayesian inference\",\"authors\":\"\",\"doi\":\"10.1016/j.cjche.2024.05.016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Modern industrial processes are typically characterized by large-scale and intricate internal relationships. Therefore, the distributed modeling process monitoring method is effective. A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder (KD-MBVAE) is introduced. Firstly, given the high consistency of relevant variables within each sub-block during the change process, the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory. Subsequently, the variational autoencoder (VAE) model was separately established, and corresponding <em>T</em><sup>2</sup> statistics were calculated. To improve fault sensitivity further, a novel statistic, derived from Kantorovich distance, is introduced by analyzing model residuals from the perspective of probability distribution. The thresholds of both statistics were determined by kernel density estimation. Finally, monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference. Additionally, a novel approach for fault diagnosis is introduced. The feasibility and efficiency of the introduced scheme are verified through two cases.</p></div>\",\"PeriodicalId\":9966,\"journal\":{\"name\":\"Chinese Journal of Chemical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chinese Journal of Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1004954124002003\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1004954124002003","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Distributed process monitoring based on Kantorovich distance-multiblock variational autoencoder and Bayesian inference
Modern industrial processes are typically characterized by large-scale and intricate internal relationships. Therefore, the distributed modeling process monitoring method is effective. A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder (KD-MBVAE) is introduced. Firstly, given the high consistency of relevant variables within each sub-block during the change process, the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory. Subsequently, the variational autoencoder (VAE) model was separately established, and corresponding T2 statistics were calculated. To improve fault sensitivity further, a novel statistic, derived from Kantorovich distance, is introduced by analyzing model residuals from the perspective of probability distribution. The thresholds of both statistics were determined by kernel density estimation. Finally, monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference. Additionally, a novel approach for fault diagnosis is introduced. The feasibility and efficiency of the introduced scheme are verified through two cases.
期刊介绍:
The Chinese Journal of Chemical Engineering (Monthly, started in 1982) is the official journal of the Chemical Industry and Engineering Society of China and published by the Chemical Industry Press Co. Ltd. The aim of the journal is to develop the international exchange of scientific and technical information in the field of chemical engineering. It publishes original research papers that cover the major advancements and achievements in chemical engineering in China as well as some articles from overseas contributors.
The topics of journal include chemical engineering, chemical technology, biochemical engineering, energy and environmental engineering and other relevant fields. Papers are published on the basis of their relevance to theoretical research, practical application or potential uses in the industry as Research Papers, Communications, Reviews and Perspectives. Prominent domestic and overseas chemical experts and scholars have been invited to form an International Advisory Board and the Editorial Committee. It enjoys recognition among Chinese academia and industry as a reliable source of information of what is going on in chemical engineering research, both domestic and abroad.