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Latent and small fault detection and diagnosis for dynamic processes

IFAC Proceedings Volumes 2008年第2期41卷 2576-2581页

作者： Zhiqiang Ge Zhihuan Song State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 Zhejiang China

Compared to large process faults, the latent and small ones are difficult to be detected. However, the accumulation of these faults may even more harmful to the process. A novel fault detection and diagnosis method is proposed which is based on similarity factor and a variable moving window. The new method is based on the idea that a change of process can be reflected in the distribution of the data, which can be detected more easily by the proposed similarity factor. Meanwhile, it has no Gaussian distribution limitation of the process data, since the mixed similarity factor is introduced. The independent component analysis (ICA) factor and the principal component analysis (PCA) factor are used for similarity comparison for Gaussian and non-Gaussian information, respectively. Besides, in order to determine the dynamic step accurately and cut the computation cost, the conventional dynamic method is modified by using autocorrelation analysis. A case study of Tennessee Eastman (TE) benchmark process shows the efficiency of the new proposed method.

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Process Automation Development in China

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IFAC Proceedings Volumes 2008年第2期41卷 10614-10616页

作者： Shuqing Wang Jian Chu Hongye Su Gang Rong Yong Gu Xiaoming Jin Quanling Zhang Lei Xie Institute of Advanced Process Control State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

A short historical view of process automation in China is provided. The development of essential aspects of process automation, including Distributed control System (DCS), Advanced Process control (APC) and Manufacturing Execution System (MES), are discussed in detail. The contribution of local process automation companies, i.e. SUPCON, and HOLLYSYS are highlighted.

关键词： Advanced Process control Model Predictive control (MPC) Manufacturing Execution System(MES) Petrochemical Industry Chemical Industry Oil Refinery Industry

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A Kernel-Based Localization Approach in Wireless Sensor Networks

A Kernel-Based Localization Approach in Wireless Sensor Netw...

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International Conference on Future Generation Communication and Networking (FGCN)

作者： Hao Wu Jiming Chen Chengqun Wang Youxian Sun State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)9781424440085

Our solution builds on a kernel-based method called the support vector machine (SVM) for determining the locations of the nodes. The basic SVM algorithm contains two steps: (1) one-region classification using the SVM; and (2) multi-region localization which is a repeated application of one-region classification for a number of different regions. In this paper, we first analyze the error effects of the choice of regions in the multi-region localization, which influences the accuracy of the localization results significantly. The realization of a choice of regions is posed as a beacon node coverage problem, i.e., the spatial distribution of the beacon nodes is determined from the coverage point of view. Second, we develop a method to arrange the regions, which we call expanded coverage region distribution, in order to avoid the problem of border effects in existing solutions. We show that expanded cover region distribution can reduce the localization errors. Our results show that, by optimally choosing and arranging the regions based on our analysis, we can significantly enhance the performance of SVM based localization. Furthermore, the optimal choice of regions to avoid the border effects can be similarly applied in other kernel-based learning methods for localization.

关键词： Wireless sensor networks Support vector machines Kernel Support vector machine classification industrial control Sun Laboratories Communication industry Process control Error analysis

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Online batch process monitoring based on multi-model ICA-PCA method

Online batch process monitoring based on multi-model ICA-PCA...

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World Congress on Intelligent control and Automation (WCICA)

作者： Zhiqiang Ge Zhihuan Song State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control University of Zhejiang Hangzhou Zhejiang China

Multiway principal component analysis (MPCA) has been widely used to monitor batch processes. However, due to the nature and complicated changes of batch processes, the data in fact contains inherent non-Gaussian information. Besides, when used for on-line monitoring, MPCA needs future value estimation. These shortcomings may lead to poor monitoring performance. In this paper, a new statistical batch process monitoring approach based on Multi-model independent component analysis (ICA) and PCA is proposed, using ICA to monitor non-Gaussian information of the process, and then PCA is applied for the rest Gaussian part. Further more, the proposed method does not require prediction of the future values, since we build sub-models for every sample time of the batch, and it can also be used for batch processes in which the batch length varies. The simulation results of penicillin batch process show the power and advantages of the proposed method, in comparison to MPCA.

关键词： Monitoring Principal component analysis Data mining Process control Independent component analysis Integrated circuit modeling Matrix decomposition

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A Process control Platform for Education in the Virtual Factory laboratory System

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IFAC Proceedings Volumes 2008年第2期41卷 9773-9778页

作者： Jishuai Wang Gang Rong Haijie Gu Qiang Wang Yiping Feng State Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 P. R. China

This paper describes an experimental platform which is useful for graduate and undergraduate education in control engineering. It contains a six-tank liquid level regulation system and a pilot distillation column, which can be used as stand-alone apparatus. Some extensions of the apparatus are made to increase the function of the platform. The compositions of distillate can be estimated by adding soft sensors to the distillation column. Integrating with liquid level regulation system makes the inlet and outlet flow of the distillation column controllable which provides a realistic engineering experimental environment. It is possible to describe the impacts of unloading from upstream and charging to downstream as in process industry. The platform has been used for graduate courses such as system identification, soft sensor designing and advanced control system.

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A Recursive Method of Identification of Hammerstein Model Based on Least Squares Support Vector Machines

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IFAC Proceedings Volumes 2008年第2期41卷 4993-4998页

作者： Kun Chen Haiqing Wang Zhihuan Song State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 P. R. China (Tel: 086-571-8795-1442-810

In the domain of industrial process modeling and control, Hammerstein model has been used widely to describe a class of nonlinear systems. Goethals et al. (2005) proposed a method based on Least Squares Support Vector Machines (LSSVM) to identify the input-output relationship of the Hammerstein model. Unfortunately, as the data points grow, this kernel learning approach costs much time correspondingly. Besides, Goethals's technique is not suitable for the on-line identification. To this end, a recursive nonlinear identification method is proposed in this paper. The basic idea is to get the recursive form of the parts of the high-dimensional matrix arisen from the optimization derivation, and get the estimation with the trick of sub-inverse matrix. With this new LSSVM approach, the Hammerstein model can be obtained recursively and much quickly, which is crucial to industrial applications that require online estimation and prediction. The simulation illustrates the validity and feasibility of the developed online identification method.

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An Improved MILP Method for Data Rectifications with Gross Error Candidates

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IFAC Proceedings Volumes 2008年第2期41卷 7528-7533页

作者： Jianlie Li Gang Rong Xu Wang Yiping Feng State Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou 310027 P. R. China (Tel: +86-571-86667032

MILP (Mixed Integer Linear Programming) method for simultaneous gross error detection and data reconciliation has been proved to be an efficient way to adjust process data with material and other balance constraints. But the efficiency will decrease significantly when the MILP method is applied in a large-scale data rectification problem because there are too many binary variables to be considered. In this paper, a strategy is proposed to generate a list of gross error candidates with reliability factors. The list of candidates are combined into the MILP objective function to improve the efficiency and accuracy through reducing the number of binary variables and giving accurate weights for suspected gross errors. industrial examples are provided to show the efficiency of the algorithm.

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Adaptive robust control of a class of uncertain nonlinear systems with unknown sinusoidal disturbances

Adaptive robust control of a class of uncertain nonlinear sy...

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IEEE Conference on Decision and control

作者： Xiangbin Liu Hongye Su Bin Yao Jian Chu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control University of Zhejiang Hangzhou China Electronic Information Engineering College Henan University of Science and Technology Luoyang China School of Mechanical Engineering Purdue University West Lafayette IN USA

ISBN: (纸本)9781424431236

In this paper, nonlinear observers are incorporated into the discontinuous projection based adaptive robust control (ARC) to synthesize performance oriented controllers for a class of uncertain nonlinear systems with unknown sinusoidal disturbances. In addition to magnitudes and phases, frequencies of the sinusoidal disturbances need not to be known as well, so long as the overall order is known. A nonlinear observer is constructed to eliminate the effect of unknown sinusoidal disturbances to improve the steady-state output tracking performance - asymptotic output tracking is achieved when the system is subjected to unknown sinusoidal disturbance only. The discontinuous projection based adaptation law is used to obtain robust estimate of all unknown parameters. In addition, a dynamic normalization signal is introduced to construct adaptive robust control laws to effectively deal with various uncertainties for a guaranteed robust performance in general. Compared with the existing internal model principle based robust adaptive designs for unknown sinusoidal disturbances, the model uncertainties considered in the paper can be of unmatched. Furthermore, in the presence of other disturbances and uncertainties in addition to the sinusoidal disturbances, the proposed approach achieves a guaranteed output tracking robust performance in terms of both the transient and the steady-state, as opposed to the robust stability results of the existing internal model principle based designs.

关键词： Programmable control Adaptive control Robust control Nonlinear systems Uncertainty Steady-state Robustness Robust stability control system synthesis Nonlinear control systems

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Robust stabilization for a class of nonlinear networked control systems

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控制理论与应用（英文版） 2008年第3期6卷 300-304页

作者： Jinfeng GAO Hongye SU Xiaofu JI Jian CHU State Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou Zhejiang 310027 China School of Electrical and Information Engineering Jiangsu University Zhenjiang Jiangsu 212013 China

The problem of robust stabilization for a class of uncertain networked control systems （NCSs） with nonlinearities satisfying a given sector condition is investigated in this paper. By introducing a new model of NCSs with parameter uncertainty, network-induced delay, nonlinearity and data packet dropout in the transmission, a strict linear matrix inequality （LMI） criterion is proposed for robust stabilization of the uncertain nonlinear NCSs based on the Lyapunov stability theory. The maximum allowable transfer interval （MATI） can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to demonstrate the applicability of the proposed algorithm.

关键词： Networked control systems （NCSs） Robust control Nonlinearity Linear matrix inequality （LMI）

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OMNeT++ based Simulation for Topology control in Wireless Sensor Network: A Case Study

OMNeT++ based Simulation for Topology Control in Wireless Se...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Jianhui Zhang Jiming Chen Jialu Fan Weiqiang Xu Youxian Sun Institute of Industrial Process Control State Key Laboratory of Industrial Control Technology University of Zhejiang Hangzhou China College of Informatics and Electronics Zhejiang University of Science and Technology Hangzhou China

ISBN: (纸本)9781424422012

Several simulation tools are used to evaluate network performance including OMNeT++. In this paper, a case of topology control (TC) in WSNs is simulated by considering its interaction with MAC layer and routing based on the SensorSimulator framework. Some ways to improve the simulation efficiency are described. Finally, the simulation results are given.

关键词： Wireless sensor networks Topology Network topology Wireless communication Protocols Routing Mathematical model

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