Junxian Li , Keke Huang , Dehao Wu , Yishun Liu , Chunhua Yang , Weihua Gui
{"title":"用于监控生产过程中连续和离散变量的混合变量字典学习","authors":"Junxian Li , Keke Huang , Dehao Wu , Yishun Liu , Chunhua Yang , Weihua Gui","doi":"10.1016/j.conengprac.2024.105970","DOIUrl":null,"url":null,"abstract":"<div><p>The fusion of industrial artificial intelligence with the Industrial Internet of Things (IIoT) can attain a heightened level of process monitoring in modern manufacturing processes. In general, the state variables of industrial processes collected through the IIoT encompass not only continuous variables but also numerous discrete variables. Owing to potential coupling factors, these variables frequently exhibit strong correlations. However, most existing methods deal only with continuous variables, which results in breaking the integrity of the state information and being incompetent to extract the useful information carried by discrete variables. To effectively address the joint monitoring challenges of continuous and discrete variables under the IIoT framework, hybrid variable dictionary learning (HVDL) is proposed in this paper. Specifically, considering that the values of discrete variables are finite sets, a specific discrete dictionary is built for data reconstruction. Besides, in order to consider the correlation between continuous and discrete variables, the alignment of them in the time dimension is achieved by sharing labels. The HVDL method can judiciously learn data dictionaries to extract multifaceted valid features across diverse data types, free from prior assumptions on data distributions. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed method, including a numerical simulation case, a closed-loop continuous stirred tank reactor benchmark, and a real zinc smelting roaster. Experimental results indicate that the proposed method can fully consider the correlation between continuous and discrete variables, thus it is conducive to identifying early anomalies and mismatch anomalies.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid variable dictionary learning for monitoring continuous and discrete variables in manufacturing processes\",\"authors\":\"Junxian Li , Keke Huang , Dehao Wu , Yishun Liu , Chunhua Yang , Weihua Gui\",\"doi\":\"10.1016/j.conengprac.2024.105970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The fusion of industrial artificial intelligence with the Industrial Internet of Things (IIoT) can attain a heightened level of process monitoring in modern manufacturing processes. In general, the state variables of industrial processes collected through the IIoT encompass not only continuous variables but also numerous discrete variables. Owing to potential coupling factors, these variables frequently exhibit strong correlations. However, most existing methods deal only with continuous variables, which results in breaking the integrity of the state information and being incompetent to extract the useful information carried by discrete variables. To effectively address the joint monitoring challenges of continuous and discrete variables under the IIoT framework, hybrid variable dictionary learning (HVDL) is proposed in this paper. Specifically, considering that the values of discrete variables are finite sets, a specific discrete dictionary is built for data reconstruction. Besides, in order to consider the correlation between continuous and discrete variables, the alignment of them in the time dimension is achieved by sharing labels. The HVDL method can judiciously learn data dictionaries to extract multifaceted valid features across diverse data types, free from prior assumptions on data distributions. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed method, including a numerical simulation case, a closed-loop continuous stirred tank reactor benchmark, and a real zinc smelting roaster. Experimental results indicate that the proposed method can fully consider the correlation between continuous and discrete variables, thus it is conducive to identifying early anomalies and mismatch anomalies.</p></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967066124001308\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124001308","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Hybrid variable dictionary learning for monitoring continuous and discrete variables in manufacturing processes
The fusion of industrial artificial intelligence with the Industrial Internet of Things (IIoT) can attain a heightened level of process monitoring in modern manufacturing processes. In general, the state variables of industrial processes collected through the IIoT encompass not only continuous variables but also numerous discrete variables. Owing to potential coupling factors, these variables frequently exhibit strong correlations. However, most existing methods deal only with continuous variables, which results in breaking the integrity of the state information and being incompetent to extract the useful information carried by discrete variables. To effectively address the joint monitoring challenges of continuous and discrete variables under the IIoT framework, hybrid variable dictionary learning (HVDL) is proposed in this paper. Specifically, considering that the values of discrete variables are finite sets, a specific discrete dictionary is built for data reconstruction. Besides, in order to consider the correlation between continuous and discrete variables, the alignment of them in the time dimension is achieved by sharing labels. The HVDL method can judiciously learn data dictionaries to extract multifaceted valid features across diverse data types, free from prior assumptions on data distributions. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed method, including a numerical simulation case, a closed-loop continuous stirred tank reactor benchmark, and a real zinc smelting roaster. Experimental results indicate that the proposed method can fully consider the correlation between continuous and discrete variables, thus it is conducive to identifying early anomalies and mismatch anomalies.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.