The Experts below are selected from a list of 21738 Experts worldwide ranked by ideXlab platform
Xuefeng Yan - One of the best experts on this subject based on the ideXlab platform.
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local global modeling and distributed computing framework for Nonlinear Plant wide process monitoring with industrial big data
IEEE Transactions on Neural Networks, 2020Co-Authors: Qingchao Jiang, Shifu Yan, Hui Cheng, Xuefeng YanAbstract:Industrial big data and complex process Nonlinearity have introduced new challenges in Plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve efficient fault detection and isolation for Nonlinear Plant-wide processes. First, a stacked autoencoder is used to extract dominant representations of each local process unit and establish the local inner monitor. Second, mutual information (MI) is used to determine the neighborhood variables of a local unit. Afterward, a joint representation learning is then performed between the local unit and the neighborhood variables to extract the outer-related representations and establish the outer-related monitor for the local unit. Finally, the outer-related representations from all process units are used to establish global monitoring systems. Given that the modeling of each unit can be performed individually, the computation process can be efficiently completed with different CPUs. The proposed modeling and monitoring method is applied to the Tennessee Eastman (TE) and laboratory-scale glycerol distillation processes to demonstrate the feasibility of the method.
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Nonlinear Plant wide process monitoring using mi spectral clustering and bayesian inference based multiblock kpca
Journal of Process Control, 2015Co-Authors: Qingchao Jiang, Xuefeng YanAbstract:Abstract Multiblock or distributed strategies are generally used for Plant-wide process monitoring, and the blocks are usually obtained based on prior process knowledge. However, process knowledge is not always available in practical application. This work aims to develop a totally data-driven distributed method for Nonlinear Plant-wide process monitoring. By performing mutual information-spectral clustering, the measured variables are automatically divided into sub-blocks that account for both linear and Nonlinear relations among variables. Considering that the variables in the same sub-block can be Nonlinearly related, kernel principal component analysis (KPCA) monitoring model is established in each sub-block. The sub-KPCA models reflect more local behaviors of a process, and the monitoring results of all blocks are combined together by Bayesian inference to provide an intuitionistic indication. The efficiency of the proposed method is demonstrated using a numerical example and the Tennessee Eastman benchmark process.
N Amanifard - One of the best experts on this subject based on the ideXlab platform.
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a neural network based sliding mode control for rotating stall and surge in axial compressors
Applied Soft Computing, 2011Co-Authors: Javadi J Moghaddam, Mehrdad H Farahani, N AmanifardAbstract:A decoupled sliding-mode neural network variable-bound control system (DSMNNVB) is proposed to control rotating stall and surge in jet engine compression systems in presence of disturbance and uncertainty. The control objective is to drive the system state to the original equilibrium point and it proves that the control system is asymptotically stable. In this controller, an adaptive neural network (NN) control scheme is employed for unknown dynamic of Nonlinear Plant without using a model of the Plant. Moreover, no prior knowledge of the Plant is assumed. The proposed DSMNNVB controller ensures Lyapunov stability of the Nonlinear dynamic of the system.
Qingchao Jiang - One of the best experts on this subject based on the ideXlab platform.
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local global modeling and distributed computing framework for Nonlinear Plant wide process monitoring with industrial big data
IEEE Transactions on Neural Networks, 2020Co-Authors: Qingchao Jiang, Shifu Yan, Hui Cheng, Xuefeng YanAbstract:Industrial big data and complex process Nonlinearity have introduced new challenges in Plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve efficient fault detection and isolation for Nonlinear Plant-wide processes. First, a stacked autoencoder is used to extract dominant representations of each local process unit and establish the local inner monitor. Second, mutual information (MI) is used to determine the neighborhood variables of a local unit. Afterward, a joint representation learning is then performed between the local unit and the neighborhood variables to extract the outer-related representations and establish the outer-related monitor for the local unit. Finally, the outer-related representations from all process units are used to establish global monitoring systems. Given that the modeling of each unit can be performed individually, the computation process can be efficiently completed with different CPUs. The proposed modeling and monitoring method is applied to the Tennessee Eastman (TE) and laboratory-scale glycerol distillation processes to demonstrate the feasibility of the method.
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Nonlinear Plant wide process monitoring using mi spectral clustering and bayesian inference based multiblock kpca
Journal of Process Control, 2015Co-Authors: Qingchao Jiang, Xuefeng YanAbstract:Abstract Multiblock or distributed strategies are generally used for Plant-wide process monitoring, and the blocks are usually obtained based on prior process knowledge. However, process knowledge is not always available in practical application. This work aims to develop a totally data-driven distributed method for Nonlinear Plant-wide process monitoring. By performing mutual information-spectral clustering, the measured variables are automatically divided into sub-blocks that account for both linear and Nonlinear relations among variables. Considering that the variables in the same sub-block can be Nonlinearly related, kernel principal component analysis (KPCA) monitoring model is established in each sub-block. The sub-KPCA models reflect more local behaviors of a process, and the monitoring results of all blocks are combined together by Bayesian inference to provide an intuitionistic indication. The efficiency of the proposed method is demonstrated using a numerical example and the Tennessee Eastman benchmark process.
Javadi J Moghaddam - One of the best experts on this subject based on the ideXlab platform.
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a neural network based sliding mode control for rotating stall and surge in axial compressors
Applied Soft Computing, 2011Co-Authors: Javadi J Moghaddam, Mehrdad H Farahani, N AmanifardAbstract:A decoupled sliding-mode neural network variable-bound control system (DSMNNVB) is proposed to control rotating stall and surge in jet engine compression systems in presence of disturbance and uncertainty. The control objective is to drive the system state to the original equilibrium point and it proves that the control system is asymptotically stable. In this controller, an adaptive neural network (NN) control scheme is employed for unknown dynamic of Nonlinear Plant without using a model of the Plant. Moreover, no prior knowledge of the Plant is assumed. The proposed DSMNNVB controller ensures Lyapunov stability of the Nonlinear dynamic of the system.
F H F Leung - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear state feedback controller for Nonlinear systems stability analysis and design based on fuzzy Plant model
IEEE Transactions on Fuzzy Systems, 2001Co-Authors: Hakkeung Lam, F H F Leung, P K S TamAbstract:This paper presents the stability analysis of a fuzzy-model-based control system consisting of a Nonlinear Plant and a Nonlinear state feedback controller and the design of the Nonlinear gains of the controller. The Nonlinear Plant is represented by a fuzzy model having p rules. A Nonlinear state feedback controller is designed to close the feedback loop. Under this design, the stability condition is reduced to p linear matrix inequalities. An application example on stabilizing a mass-spring-damper system will be given.
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design of fuzzy controllers for uncertain Nonlinear systems using stability and robustness analyses
Systems & Control Letters, 1998Co-Authors: F H F LeungAbstract:This paper analyzes the stability and robustness of uncertain Nonlinear systems and shows that the analysis results provide an efficient technique for the design of fuzzy controllers. Based on a fuzzy Plant model describing an uncertain Nonlinear Plant, this design involves the derivation of a stability criterion and a robust area in the uncertain parameter space in terms of some measures of the closed-loop control system matrices. An application example on balancing an inverted pendulum is given to illustrate the simple design methodology, the stability and the robustness of the feedback system incorporated with the proposed fuzzy controller.
