Fault estimation for multi-rate descriptor systems using bi-directional long short-term memory neural network

IF 1.6 4区 工程技术 Q3 ENGINEERING, CHEMICAL
Dhrumil Gandhi, Meka Srinivasarao
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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.

基于双向长短期记忆神经网络的多速率广义系统故障估计
由于多速率测量带来的复杂性,多速率广义系统的故障估计尤其具有挑战性,该系统涉及微分和代数状态。提出了一种将基于微分代数方程的扩展卡尔曼滤波与双向长短期记忆神经网络相结合的故障估计方法。利用DAE-EKF生成多速率残差,作为神经网络估计故障的输入。双lstm网络利用过去和未来的信息,在lstm的基础上,通过向前和向后处理数据来改进lstm。这种双向方法增强了时间依赖性捕获,使双lstm成为准确故障估计的理想选择。通过对两相反应器-冷凝器循环系统和反应精馏系统的仿真研究,验证了该方法的有效性。与传统前馈神经网络的DAE-EKF和LSTM的DAE-EKF相比,该方法对多速率广义系统的故障估计能力更强。
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来源期刊
Canadian Journal of Chemical Engineering
Canadian Journal of Chemical Engineering 工程技术-工程:化工
CiteScore
3.60
自引率
14.30%
发文量
448
审稿时长
3.2 months
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.
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