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Soft sensor modeling of ball mill load via principal component analysis and support vector machines

7th International Symposium on Neural Networks, ISNN 2010

作者： Tang, Jian Zhao, Lijie Yu, Wen Yue, Heng Chai, Tianyou Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang 110819 China College of Information Engineering Shenyang Institute of Chemical Technology Shenyang 110142 China Departamento de Control Automatico CINVESTAV-IPN Av.IPN 2508 México D.F. 07360 Mexico

ISBN: (纸本)9783642129896

In wet ball mill, measurement accuracy of mill load (ML) is very important. It affects production capacity and energy efficiency. A soft sensor method is proposed to estimate the mill load in this paper. Vibration signal of mill shell in time domain is first transformed into power spectral density (PSD) using fast Fourier transform (FFT), such that the relative amplitudes of different frequencies contain mill load information directly. Feature variables at low, medium and high frequency bands are extracted through principal component analysis (PCA), which selects input as a preprocessing procedure to improve the modeling performance. Three support vector machine (SVM) models are built to predict the mill operating parameters. A case study shows that proposed soft sensor method has higher accuracy and better predictive performance than the other normal approaches. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Principal component analysis

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Absolute stability of Lur'e singularly perturbed systems with multiple nonlinearities

Absolute stability of Lur'e singularly perturbed systems wit...

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作者： Yang, Chunyu Zhang, Qingling Chou, Jyh-Horng Zhang, Yingwei Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang 110004 China Institute of Systems Science Northeastern University Shenyang 110004 China Institute of System Information and Control Engineering National Kaohsiung First University of Science and Technology I University Road Yuanchau Kaohsiung 824 Taiwan

ISBN: (纸本)9781424474264

This paper investigates the absolute stability problem for Lur'e singularly perturbed systems with multiple nonlinearities. The objective is to determine if the system is absolutely stable for any Ε ∈ (0, Ε0], where Ε denotes the perturbation parameter and Ε0 is a pre-defined positive scalar. Firstly, an Ε-dependent Lyapunov function of Lur'e-postnikov form is constructed. Then, a stability criterion expressed in terms of Ε-independent linear matrix inequalities (LMIs) is derived. Finally, an example is given to show the feasibility and effectiveness of the obtained method. © 2010 AACC.

关键词： Stability criteria

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Estimation of component concentrations of sodium aluminate solution via PLS and Hammerstein recurrent neural networks

Estimation of component concentrations of sodium aluminate s...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Wei Wang Lijie Zhao Tianyou Chai Wen Yu Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University China Key Laboratory of Integrated Automation of Process Industry Shenyang Institute of Chemical Technology Information Engineering School Ministry of Education China Key Laboratory of Integrated Automation of Process Industry Ministry of Education and Research Center of Automation Northeastern University China Departamento de Control Automatico CINVESTAV Mexicali Mexico

In this paper, a new on-line soft sensing method is proposed for component concentrations of sodium aluminate solution. With this sensing strategy, real-time control and optimization can be realized in aluminate production plants. Several advance techniques are used, such as PLS (Partial Least Squares), Hammerstein model, recurrent neural networks and least square algorithm. Industrial experiment results show that the proposed soft sensing algorithm is effective.

关键词： Nonlinear dynamical systems Recurrent neural networks Computational modeling Heuristic algorithms Temperature measurement Data models Artificial neural networks

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Low-complexity polynomial approximation of explicit MPC via linear programming

Low-complexity polynomial approximation of explicit MPC via ...

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American control Conference (ACC)

作者： Michal Kvasnica Johan Löfberg Martin Herceg L'uboš C̆irka Miroslav Fikar Automation and Mathematics Slovak University of Technology in Bratislava Slovakia Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology Slovakia Automatic Control Laboratory Linköpings universitet Linköpings Sweden

This paper addresses the issue of the practical implementation of Model Predictive controllers (MPC) to processes with short sampling times. Given an explicit solution to an MPC problem, the main idea is to approximate the optimal control law defined over state space regions by a single polynomial of pre-specified degree which, when applied as a state-feedback, guarantees closed-loop stability, constraint satisfaction, and a bounded performance decay. It is shown how to search for such a polynomial by solving a single linear program.

关键词： Polynomials Linear programming Sampling methods Optimal control Stability Table lookup automatic control Feedback State-space methods Chemical technology

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Simulation and test of backscattering from nonspherical soot particles swarm using laser

Simulation and test of backscattering from nonspherical soot...

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Russian-Chinese Symposium on Laser Physics and Laser Technologies (RCSLPLT) and Academic Symposium on Optoelectronics Technology (ASOT)

作者： Xing Jian Zhao Jiaqun Liu Yang Sun Xiaogang Insititute of Automatic Detecting and Process Control System Harbin Institute of Technology Harbin China State Key Laboratory of Tunable Laser Technology Harbin Institute of Technology Harbin China College of Science Harbin Engineering University Harbin China

When the backscattering method is adopted to test the concentration and the size of soot, it's very important to analyze the backscattering characteristics of soot to determine the optimal light source, detector and detection angle. In this paper, the backscattering characteristics of nonspherical soot particles swarm are investigated. The size and shape of soot particles are determined by microscopic measurement. According to the micrograph, both Ellipsoid model and generalized Chebyshev models are selected to imitate soot particles. With the Monte-Carlo method, a random oriented particles swarm is created, and backscattering relative intensity is calculated by T-matrix method with different parameters. A simple backscattering test system was established, in which the light source and detector are selected based on the calculation results. The numerical and experimental results show that the combination of the Monte-Carlo method and the T-matrix calculation can be successfully applied to simulate the backscattering of the nonspherical particles swarm, and the experimental system has higher backscattering intensity according to simulation result. It provides a reference for improving the measurement accuracy of particles size and concentration.

关键词： Backscatter Scattering Particle measurements Atmospheric measurements Detectors Light sources Simulation

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Improved multidisciplinary multiobjective collaborative optimization technique based on nsga-ii algorithm

Improved multidisciplinary multiobjective collaborative opti...

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IFAC International Workshop on "Supplemental Ways for Improving International Stability", SWIIS 2010 - Proceedings

作者： Li, H. Jing, Y. Zhang, S. Ojleska, V. Kolemisevska, T. Dimirovski, G.M. Key Laboratory of Integrated Automation of Process Industry Northeastern University Ministry of Education Shenyang 110004 China School of Information Science and Engineering Northeastern University 110004 Shenyang China Institute of ASE School FEIT SS Cyril and Methodius University Karpos II BB MK-1000 Skopje Macedonia Computer Eng. and Control Eng. Departments Dogus University TR-34722 Istanbul Turkey

ISBN: (纸本)9783902661920

The multiobjective collaborative optimization (MCO) has been widely adopted in concurrent engineering design as a good multiobjective optimization approach provided the problem of optimization results converging often to a local extremum has been taken care of. It has been demonstrated to yield useful solutions to a wide range of optimization problems under concurrent condition both technical and non-technical ones. A solution strategy for MCO that overcomes this drawback is proposed here on the grounds of genetic NSGA-II algorithm. It is an adaptive heuristic search technique that controls the distances between the individual and system's feasible region during genetic evolution. In the evolving NSGA-II based non-dominated sorting, the individual threshold of infeasibility degree is reduced gradually via genetic evolution and the individual's feasibility capacity is determined by its infeasibility degree and the threshold. It has been applied to a benchmark example of speed reducer design, and the obtained results demonstrate the proposed technique is comparably more efficient and may offer substantial advantages over the gradient-based optimization methods. © 2010 IFAC.

关键词： Multiobjective optimization

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An Optimal control Scheme for a Class of Discrete-time Nonlinear Systems with Time Delays Using Adaptive Dynamic Programming

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Acta automatica Sinica 2010年第1期36卷 121-129页

作者： Qing-Lai WEI Hua-Guang ZHANG De-Rong LIU Yan ZHAO Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 P. R. China School of Information Science and Engineering Northeastern University Shenyang 110004 P. R. China Department of Automatic Control Engineering Shenyang Institute of Engineering Shenyang 110136 P. R. China

Abstract In this paper, an optimal control scheme for a class of nonlinear systems with time delays in both state and control variables with respect to a quadratic performance index function is proposed using a new iterative adaptive dynamic programming (ADP) algorithm. By introducing a delay matrix function, the explicit expression of the optimal control is obtained using the dynamic programming theory and the optimal control can iteratively be obtained using the adaptive critic technique. Convergence analysis is presented to prove that the performance index function can reach the optimum by the proposed method. Neural networks are used to approximate the performance index function, compute the optimal control policy, solve delay matrix function, and model the nonlinear system, respectively, for facilitating the implementation of the iterative ADP algorithm. Two examples are given to demonstrate the validity of the proposed optimal control scheme.

关键词： Adaptive dynamic programming (ADP) approximate dynamic programming time delay optimal control nonlinear system neural networks

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Identification of nonlinear static processes with local polynomial regression and subset selection

Identification of nonlinear static processes with local poly...

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15th IFAC Symposium on System Identification, SYSID 2009

作者： Sequenz, Heiko Schreiber, Alexander Isermann, Rolf Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University Darmstadt Germany

ISBN: (纸本)9783902661470

The presented method for nonlinear system identification is based on the LOLIMOT algorithm introduced by Nelles and Isermann [1996]. The LOLIMOT algorithm divides the input space by a tree-construction algorithm and interpolates the local linear models by local membership functions. Instead of assuming local linear models, the presented algorithm utilizes general local nonlinear functions, which make the algorithm more flexible. These are approximated by a multidimensional Taylor series. Since the amount of regressors grows fast with the number of inputs and the expansion order, a subset selection procedure is introduced. It reveals significant regressors and gives information about the local functional behavior. The local subset selection is implemented as a stepwise regression with replacement of regressors. Mallows' Cp-statistic is used for the subset selection algorithm and is also implemented for final model selection. The benefit of the extended algorithm lies in the higher flexibility in the local models, which results in less partitions of the input space by a similar approximation quality. © 2009 IFAC.

关键词： Membership functions

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Fault tolerant actuators and drives - Structures, fault detection principles and applications

Fault tolerant actuators and drives - Structures, fault dete...

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7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEprocess'09

作者： Muenchhof, Marco Beck, Mark Isermann, Rolf Technische Universität Darmstadt Institute of Automatic Control Laboratory for Control Systems and Process Automation Darmstadt Germany

ISBN: (纸本)9783902661463

As fault detection and fault diagnosis are more and more finding their way into modern industrial mechatronic products, it is now time to take the next step. Based on the research efforts for fault detection and diagnosis, a platform has been prepared for research on fault management, i.e. automatic reactions of the system to continue operation after the detection of faults. These reactions may employ hardware redundancy (i.e. switching from a faulty actuator to another, intact one) or analytical redundancy (i.e. switching from a faulty sensor to a "model sensor" or "soft sensor"). A total fault tolerance concept must encompass all components of a system, i.e. the actuators and drives, the sensors as well as the controller and communication. In many cases, a degradation of functions has to be accepted after a fault has appeared. This contribution will center on actuators and drives showing real-world examples of prototypical realizations and examples from industrial products. Concentrating on the most widespread actuation principles, the paper will focus on electric drives and hydraulic actuators. First, a review is given on fault tolerance principles and general structural considerations e.g. hot-standby and cold-standby, focusing on the scheme of an overall fault-tolerant control system. Then, fault statistics for real actuators and drives will be presented. These fault statistics give hints on the parts of the actuators that are most susceptible to faults. Different designs of fault tolerant actuators and drives, which have been realized, shall be presented and evaluated with respect to their capabilities of sustaining faults. Finally, an outlook for fault-tolerant mechatronic systems will be given. © 2009 IFAC.

关键词： Aerospace applications

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Neural-net model based fault diagnosis for hydraulic servo axes

Neural-net model based fault diagnosis for hydraulic servo a...

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作者： Muenchhof, Marco Clever, Sebastian Technische Universität Darmstadt Institute of Automatic Control Laboratory for Control Systems and Process Automation 64283 Darmstadt Germany

ISBN: (纸本)9783902661463

In this paper, fault detection methods for hydraulic systems based on a parity equation approach with neural net models are presented. Hydraulic systems are used in manifold applications in industry. They are however not yet the subject of intense research in the area of fault detection and diagnosis, which can be mainly attributed to their strong nonlinear behavior, which exacerbates the physical modeling extensively. To avoid the difficulties associated with the physical modeling, a data-driven modeling approach based on the LOLIMOT neural network will be presented in this paper. Different subsystems of the hydraulic servo axis will be modeled using different sensor configurations. Experimental data from a real testbed allow to compare the model fidelity of the different resulting neural nets and can also be used to illustrate the capabilities of the parity-equation based fault detection approach, which in general allows the detection of tiny faults, such as sensor offset faults in the area of a few percent of the maximum sensor readout. © 2009 IFAC.

关键词： Fault detection

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