{"title":"Fault estimation for multi-rate descriptor systems using bi-directional long short-term memory neural network","authors":"Dhrumil Gandhi, Meka Srinivasarao","doi":"10.1002/cjce.25577","DOIUrl":null,"url":null,"abstract":"<p>Fault estimation in multi-rate descriptor systems, which involve both differential and algebraic states, is particularly challenging due to the complexity introduced by multi-rate measurements. This paper proposes a novel fault estimation approach that combines a differential-algebraic equation based extended Kalman filter (DAE-EKF) with a bi-directional long short-term memory (bi-LSTM) neural network. The DAE-EKF is used to generate multi-rate residuals, which serve as inputs to neural networks to estimate faults. bi-LSTM networks improve upon LSTMs by processing data in both forward and backward directions, using past and future information. This bidirectional approach enhances temporal dependency capture, making bi-LSTMs ideal for accurate fault estimation. The efficacy of the proposed method is demonstrated using simulation studies on a two-phase reactor-condenser system with recycle and a reactive distillation system. The proposed approach has shown superior fault estimation capability for multi-rate descriptor systems compared to DAE-EKF with conventional feedforward neural networks and DAE-EKF with LSTM.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 7","pages":"3247-3269"},"PeriodicalIF":1.6000,"publicationDate":"2024-12-11","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.25577","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}
引用次数: 0
Abstract
Fault estimation in multi-rate descriptor systems, which involve both differential and algebraic states, is particularly challenging due to the complexity introduced by multi-rate measurements. This paper proposes a novel fault estimation approach that combines a differential-algebraic equation based extended Kalman filter (DAE-EKF) with a bi-directional long short-term memory (bi-LSTM) neural network. The DAE-EKF is used to generate multi-rate residuals, which serve as inputs to neural networks to estimate faults. bi-LSTM networks improve upon LSTMs by processing data in both forward and backward directions, using past and future information. This bidirectional approach enhances temporal dependency capture, making bi-LSTMs ideal for accurate fault estimation. The efficacy of the proposed method is demonstrated using simulation studies on a two-phase reactor-condenser system with recycle and a reactive distillation system. The proposed approach has shown superior fault estimation capability for multi-rate descriptor systems compared to DAE-EKF with conventional feedforward neural networks and DAE-EKF with LSTM.
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.