The Experts below are selected from a list of 54723 Experts worldwide ranked by ideXlab platform
Chunhui Zhao - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Distributed Monitoring Strategy for Large-Scale Nonstationary Processes Subject to Frequently Varying Conditions Under Closed-Loop Control
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Chunhui Zhao, He SunAbstract:Large-scale Processes under closed-loop control are commonly subjected to frequently varying conditions due to load changes or other causes, resulting in typical nonstationary characteristics. For closed-loop control Processes, the normal changes in operation conditions may distort the static and dynamic variations in a different way from Real faults. In this paper, a dynamic distributed monitoring strategy is proposed to separate the dynamic variations from the steady states, and concurrently, monitor them to distinguish changes in the normal operating condition and Real faults for large-scale nonstationary Processes under closed-loop control. First, large-scale nonstationary Process variables are decomposed into different blocks to mine the local information. Second, the static and dynamic equilibrium relations are separated by probing into the cointegration analysis solution in each block. Third, the concurrent monitoring models are constructed to supervise both the steady variations and their dynamic counterparts for each block. Finally, the local monitoring results are combined by Bayesian inference to obtain global results, which enable description and monitoring of both static and dynamic equilibrium relations from the global and local viewpoints. The feasibility and performance of the proposed method are illustrated with a Real Industrial Process, which is a 1000-MW ultra-supercritical thermal power unit.
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distributed dynamic modeling and monitoring for large scale Industrial Processes under closed loop control
arXiv: Systems and Control, 2018Co-Authors: Wenqing Li, Chunhui Zhao, Biao HuangAbstract:For large-scale Industrial Processes under closed-loop control, Process dynamics directly resulting from control action are typical characteristics and may show different behaviors between Real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the closed-loop control mechanism and only explore static characteristics, which thus are incapable of distinguishing between Real Process faults and nominal changes of operating conditions, leading to unnecessary alarms. In this regard, this paper proposes a distributed monitoring method for closed-loop Industrial Processes by concurrently exploring static and dynamic characteristics. First, the large-scale closed-loop Process is decomposed into several subsystems by developing a sparse slow feature analysis (SSFA) algorithm which capture changes of both static and dynamic information. Second, distributed models are developed to separately capture static and dynamic characteristics from the local and global aspects. Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on Process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other. Case studies are conducted based on both benchmark data and Real Industrial Process data to illustrate the effectiveness of the proposed method.
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sparse exponential discriminant analysis and its application to fault diagnosis
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Chunhui ZhaoAbstract:Discriminant analysis, as a popular supervised classification method, has been successfully used in fault diagnosis, which, however, involves a linear combination of all variables, and thus may result in poor model interpretability and inaccurate classification performance. In this paper, a sparse exponential discriminant analysis (SEDA) algorithm is proposed for addressing those issues. The sparse discriminant model is developed by introducing the penalty of lasso or elastic net into the exponential discriminant analysis algorithm, so that the key variables responsible for the fault can be automatically selected. Since the formulated model is nonconvex, it is recast as an iterative convex optimization problem using the minorization–maximization algorithm. After that, a feasible gradient direction method is developed to solve the optimization problem effectively. The sparse solutions indicate the key faulty information to improve classification performance, and thus distinguish different faults more accurately. A simulation Process and a Real Industrial Process are used to test the performance of the proposed method, and the experimental results show that the SEDA algorithm can isolate the faulty variables and simplify the discriminant model by discarding variables with little significance.
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A sparse dissimilarity analysis algorithm for incipient fault isolation with no priori fault information
Control Engineering Practice, 2017Co-Authors: Chunhui Zhao, Furong GaoAbstract:The conventional multivariate statistical Process control (MSPC) methods in general quantify the distance between the new sample and the modelling samples for fault detection and diagnosis, which, however, do not check the changes of data distribution as long as monitoring statistics stay inside normal region enclosed by control limit and thus are not sensitive to incipient changes. In the present work, a sparse dissimilarity (SDISSIM) algorithm is developed which can isolate the incipient abnormal variables that change the data distribution structure and does not need any priori fault knowledge. First, the distribution dissimilarity is decomposed deeply and significant dissimilarity is extracted to integrate the critical difference of variable covariance structure between the reference normal operation distribution and the actual distribution. Second, a sparse regression-based optimization problem is formulated to isolate abnormal variables associated with changes of distribution structure. Sparse coefficients are obtained with only a small fraction of variables’ coefficients nonzeros, pointing to abnormal variables. As illustrations, SDISSIM is applied to both simulated and Real Industrial Process data with encouraging results to figure out the slight distortions.
Biao Huang - One of the best experts on this subject based on the ideXlab platform.
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distributed dynamic modeling and monitoring for large scale Industrial Processes under closed loop control
arXiv: Systems and Control, 2018Co-Authors: Wenqing Li, Chunhui Zhao, Biao HuangAbstract:For large-scale Industrial Processes under closed-loop control, Process dynamics directly resulting from control action are typical characteristics and may show different behaviors between Real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the closed-loop control mechanism and only explore static characteristics, which thus are incapable of distinguishing between Real Process faults and nominal changes of operating conditions, leading to unnecessary alarms. In this regard, this paper proposes a distributed monitoring method for closed-loop Industrial Processes by concurrently exploring static and dynamic characteristics. First, the large-scale closed-loop Process is decomposed into several subsystems by developing a sparse slow feature analysis (SSFA) algorithm which capture changes of both static and dynamic information. Second, distributed models are developed to separately capture static and dynamic characteristics from the local and global aspects. Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on Process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other. Case studies are conducted based on both benchmark data and Real Industrial Process data to illustrate the effectiveness of the proposed method.
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robust probabilistic principal component analysis for Process modeling subject to scaled mixture gaussian noise
Computers & Chemical Engineering, 2016Co-Authors: Anahita Sadeghian, Biao HuangAbstract:Abstract Conventionally, for probabilistic principal component analysis (PPCA) based regression models, noise with a Gaussian distribution is assumed for both input and output observations. This assumption makes the model to be vulnerable to large random errors, known as outliers. In this article, unlike the conventional noise assumption, a mixture noise model with a contaminated Gaussian distribution is adopted for probabilistic modeling to diminish the adverse effect of outliers, which usually occur due to irregular Process disturbances, instrumentation failures or transmission problems. This is done by downweighing the effect of the noise component which accounts for contamination on output prediction. Outliers are common in Process industries; therefore, handling this issue is of practical importance. In comparison with conventional PPCA based regression model, prediction performance of the developed robust probabilistic regression model is improved in presence of data contamination. To evaluate the model performance two case studies were carried out. A simulated set of data with specific characteristics to highlight the presence of outliers was used to demonstrate the robustness of the developed model. The advantages of this robust model are further illustrated via a set of Real Industrial Process data.
He Sun - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Distributed Monitoring Strategy for Large-Scale Nonstationary Processes Subject to Frequently Varying Conditions Under Closed-Loop Control
IEEE Transactions on Industrial Electronics, 2019Co-Authors: Chunhui Zhao, He SunAbstract:Large-scale Processes under closed-loop control are commonly subjected to frequently varying conditions due to load changes or other causes, resulting in typical nonstationary characteristics. For closed-loop control Processes, the normal changes in operation conditions may distort the static and dynamic variations in a different way from Real faults. In this paper, a dynamic distributed monitoring strategy is proposed to separate the dynamic variations from the steady states, and concurrently, monitor them to distinguish changes in the normal operating condition and Real faults for large-scale nonstationary Processes under closed-loop control. First, large-scale nonstationary Process variables are decomposed into different blocks to mine the local information. Second, the static and dynamic equilibrium relations are separated by probing into the cointegration analysis solution in each block. Third, the concurrent monitoring models are constructed to supervise both the steady variations and their dynamic counterparts for each block. Finally, the local monitoring results are combined by Bayesian inference to obtain global results, which enable description and monitoring of both static and dynamic equilibrium relations from the global and local viewpoints. The feasibility and performance of the proposed method are illustrated with a Real Industrial Process, which is a 1000-MW ultra-supercritical thermal power unit.
Antonio De Padua Braga - One of the best experts on this subject based on the ideXlab platform.
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online learning of neural networks using random projections and sliding window a case study of a Real Industrial Process
Engineering Applications of Artificial Intelligence, 2021Co-Authors: Wagner J Alvarenga, Felipe V Campos, Vitor M Hanriot, Eduardo B Goncalves, Alexsander C A A Costa, Lourenco R G Araujo, Eduardo Magalhaes, Antonio De Padua BragaAbstract:Abstract Online Learning of non-stationary data streams is a challenging task. This work presents an online training method for a Single hidden Layer Feedforward neural Network (SLFN) that learns sample-by-sample, using an adjustable sliding window to adapt the network when data has changed. The method presents a fast training procedure, estimating hidden and output layer parameters independently. Tests with four synthetic datasets showed a good accuracy and quick recovery after drift occurrences. The proposed method is also applied to a Real dataset from an Industrial Process in order to address the anomaly detection task, with the network acting as a classifier. Results show that the method is able to detect drifts prior to anomalies in the pre-fault periods, in the Real situation that appeared in the Industrial dataset.
Yonghua Zhou - One of the best experts on this subject based on the ideXlab platform.
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one step facile synthesis of graphene oxide tio2 composite as efficient photocatalytic membrane for water treatment crossflow filtration operation and membrane fouling analysis
Chemical Engineering and Processing, 2017Co-Authors: Chenyua Zhu, Gonggang Liu, Shichao Wei, Yonghua ZhouAbstract:Abstract Graphene oxide (GO) has been continuously demonstrated as promising membrane for water purification due to its excellent hydrophilic surface properties and special interconnected 2D nanofluidic channels for ion/molecule transport. In this work, to resolve the membrane fouling problem for GO membrane, GO/TiO 2 membrane with the multifunction of concurrent water filtration and photodegradation for pollutants was successfully fabricated by a one-step facile approach. Crossflow filtration was applied to evaluate the separation performance by simulating the Real Industrial Process. The as-prepared GO/TiO 2 membrane exhibited remarkable ability on photocatalytic degradation of Methylene Blue under UV light, 92% of MB could be degraded after 110 min irradiation with 4 mg of photocatalyst. The membrane fouling could be effectively alleviated with UV light irradiation, resulting in a high flux recoverability of 96% after 100 min. Furthermore, the mechanism of membrane fouling Processes for GO/TiO 2 membrane under crossflow filtration was analyzed based on Darcylaw model. The result showed adsorption and cake layer which accounted for 46.3% and 46.0% of membrane resistance were main factors that cause GO/TiO 2 membrane fouling. The excellent performance of GO/TiO 2 membrane provided valuable insight for its Industrial application in clean water production field.
