{"title":"利用 LSTM 和注意力的新型故障诊断框架:田纳西伊士曼工艺案例研究","authors":"Shuaiyu Zhao, Yiling Duan, Nitin Roy, Bin Zhang","doi":"10.1002/cjce.25460","DOIUrl":null,"url":null,"abstract":"<p>In the era of Industry 4.0, substantial research has been devoted to the field of fault detection and diagnosis (FDD), which plays a critical role in preventive maintenance of large chemical processes. However, the existing studies are primarily focused on few-shot samples of process data and without considering the role of activation functions in temporal diagnostic tasks. In this paper, an end-to-end chemical fault diagnosis framework that combines bidirectional long short-term memory (LSTM) with attention mechanism is proposed. In the preprocessing stage, a special sliding time window function is developed to integrate multivariate samples containing complex temporal information via operation such as subset extraction. Afterwards, the bidirectional LSTM is constructed to address dynamic and temporal relationship on longer series observation, and the attention mechanism is adopted to highlight key fault features by assigning different attention weights. A case application is performed on the enriched Tennessee Eastman process (TEP), which reduces the bias between sample statistics and larger population parameters compared to existing few-shot sample studies. The metric evaluation experiments for six activations show that the model configured with tanh function can achieve the optimal tradeoff in chemical process tasks, providing a strong benchmark for subsequent fault diagnosis research.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 4","pages":"1763-1785"},"PeriodicalIF":1.6000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel fault diagnosis framework empowered by LSTM and attention: A case study on the Tennessee Eastman process\",\"authors\":\"Shuaiyu Zhao, Yiling Duan, Nitin Roy, Bin Zhang\",\"doi\":\"10.1002/cjce.25460\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In the era of Industry 4.0, substantial research has been devoted to the field of fault detection and diagnosis (FDD), which plays a critical role in preventive maintenance of large chemical processes. However, the existing studies are primarily focused on few-shot samples of process data and without considering the role of activation functions in temporal diagnostic tasks. In this paper, an end-to-end chemical fault diagnosis framework that combines bidirectional long short-term memory (LSTM) with attention mechanism is proposed. In the preprocessing stage, a special sliding time window function is developed to integrate multivariate samples containing complex temporal information via operation such as subset extraction. Afterwards, the bidirectional LSTM is constructed to address dynamic and temporal relationship on longer series observation, and the attention mechanism is adopted to highlight key fault features by assigning different attention weights. A case application is performed on the enriched Tennessee Eastman process (TEP), which reduces the bias between sample statistics and larger population parameters compared to existing few-shot sample studies. The metric evaluation experiments for six activations show that the model configured with tanh function can achieve the optimal tradeoff in chemical process tasks, providing a strong benchmark for subsequent fault diagnosis research.</p>\",\"PeriodicalId\":9400,\"journal\":{\"name\":\"Canadian Journal of Chemical Engineering\",\"volume\":\"103 4\",\"pages\":\"1763-1785\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Journal of Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cjce.25460\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cjce.25460","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
A novel fault diagnosis framework empowered by LSTM and attention: A case study on the Tennessee Eastman process
In the era of Industry 4.0, substantial research has been devoted to the field of fault detection and diagnosis (FDD), which plays a critical role in preventive maintenance of large chemical processes. However, the existing studies are primarily focused on few-shot samples of process data and without considering the role of activation functions in temporal diagnostic tasks. In this paper, an end-to-end chemical fault diagnosis framework that combines bidirectional long short-term memory (LSTM) with attention mechanism is proposed. In the preprocessing stage, a special sliding time window function is developed to integrate multivariate samples containing complex temporal information via operation such as subset extraction. Afterwards, the bidirectional LSTM is constructed to address dynamic and temporal relationship on longer series observation, and the attention mechanism is adopted to highlight key fault features by assigning different attention weights. A case application is performed on the enriched Tennessee Eastman process (TEP), which reduces the bias between sample statistics and larger population parameters compared to existing few-shot sample studies. The metric evaluation experiments for six activations show that the model configured with tanh function can achieve the optimal tradeoff in chemical process tasks, providing a strong benchmark for subsequent fault diagnosis research.
期刊介绍:
The Canadian Journal of Chemical Engineering (CJChE) publishes original research articles, new theoretical interpretation or experimental findings and critical reviews in the science or industrial practice of chemical and biochemical processes. Preference is given to papers having a clearly indicated scope and applicability in any of the following areas: Fluid mechanics, heat and mass transfer, multiphase flows, separations processes, thermodynamics, process systems engineering, reactors and reaction kinetics, catalysis, interfacial phenomena, electrochemical phenomena, bioengineering, minerals processing and natural products and environmental and energy engineering. Papers that merely describe or present a conventional or routine analysis of existing processes will not be considered.