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Multi-Model Based Fault Detection and Diagnosis of a Hydraulic Servo Axis

IFAC Proceedings Volumes 2008年第2期41卷 13117-13117页

作者： Marco Muenchhof Technische Universitaet Darmstadt Institute of Automatic Control Laboratory for Control Systems and Process Automation Darmstadt Germany (Tel: +49-6151-16-3114

This contribution describes the design and implementation of a multi-model based fault detection and diagnosis system for a hydraulic servo axis. Multiple models are operated in parallel to the plant - similar to a bank of observers. Beside one model mimicking the fault free process, there are additional models describing the process behavior in the presence of both actuator and process faults. For each model the squared error between the model output and the plant output is determined. The model which (over a period of time) shows the best match with the process is assumed to currently best describe the process behavior. The fault injected into the model with the best match is thus at the same time the diagnosis, i.e. the fault which is assumed to be present in the real system. This system has been tested exhaustively at a testbed. The result of the tests is shown in the accompanying video.

关键词： Hydraulic Actuators Fault Diagnosis Physical Models Video Demonstration

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Further results on stability criteria for linear systems with time-varying delay

Further results on stability criteria for linear systems wit...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jian Sun G.P. Liu Jie Chen Faculty of Advanced Technology University of Glamorgan Pontypridd UK Department of Automatic Control Beijing Institute of Technology Beijing P.R. China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing P.R. China

The issue of stability of linear systems with time-varying delay is considered in this paper. By constructing a new type of Lyapunov functional which contains a novel triple integral term and using Finsler's lemma, new delay-dependent stability criteria are derived in terms of linear matrix inequality (LMI). Numerical examples are given to illustrate the effectiveness of the proposed method.

关键词： Stability criteria Linear systems Time varying systems Delay systems Symmetric matrices Linear matrix inequalities Delay estimation automatic control Information science Laboratories

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Performance evaluation of Kalman filter for fault detection of hydraulic actuators

Performance evaluation of Kalman filter for fault detection ...

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作者： Muenchhof, Marco Isermann, Rolf Laboratory for Control Systems and Process Automation Institute of Automatic Control TU Darmstadt Germany

ISBN: (纸本)9783902661241

In this paper, an Extended Kalman-Bucy-Filter approach for fault detection and diagnosis of an hydraulic servo-axis is presented. The filter improves the results with the EKF as reported in literature since it uses a new model structure, considers nonlinear valve spool displacement-flow relationships and incorporates temperature effects. The performance of the newly proposed architecture is evaluated thoroughly at a testbed in the presence of both process faults and sensor faults. For sensor faults, the smallest reliably detectable sensor fault is determined. The method is finally compared to parity equations and parameter estimation, which have also successfully been applied to the supervision oif hydraulic servo axes. Copyright © 2007 IFAC.

关键词： Fault detection

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PERFORMANCE EVALUATION OF KALMAN FILTER FOR FAULT DETECTION OF HYDRAULIC ACTUATORS

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IFAC Proceedings Volumes 2007年第7期40卷 692-697页

作者： Marco Muenchhof Rolf Isermann Institute of Automatic Control Laboratory for Control Systems and Process Automation TU Darmstadt Germany

关键词： Hydraulic Actuator Fault Detection Extended Kalman-Bucy Filter Nonlinear Parameter Estimation

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Identification methods for experimental nonlinear modeling of combustion engines

Identification methods for experimental nonlinear modeling o...

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作者： Schreiber, Alexander Isermann, Rolf Laboratory of Control Engineering and Process Automation Institute of Automatic Control Darmstadt University of Technology Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany

ISBN: (纸本)9783902661265

The optimized control of combustion engines with regard to minimized fuel consumption and emissions requires nonlinear models. Because of an increase of control inputs, like fuel mass flow, injection angle, exhaust gas recirculation flow and several outputs like torque, nitrogen oxides (NOx), hydrocarbons (HC) and particulates the classical grid-based measurement techniques take too long time and do not include dynamics. Therefore different measurement strategies for the stationary and dynamic behavior are described, like Design of Experiments (DoE) and use of suitable neural networks and Pseudo-Random-Binary-Signals (PRBS). As the structure of the models is not precisely known a-priori, nonlinear identification methods in form of special versions of neural networks are good candidates. Therefore, it will be shown how with special amplitude-modulated pseudo random binary signals (APRBS), simultaneous excitation of several input signals, nonlinear multi-input multi-output models can be obtained in relatively short time. Copyright © 2006 IFAC.

关键词： Nitrogen oxides

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

No abstract available

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IDENTIFICATION METHODS FOR EXPERIMENTAL NONLINEAR MODELING OF COMBUSTION ENGINES

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IFAC Proceedings Volumes 2007年第10期40卷 351-357页

作者： Alexander Schreiber Rolf Isermann Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49 6151 16 2114 Fax: +49 6151 16 6114

关键词： Engine modeling nonlinear models neural networks dynamic modeling test generation

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Advances in Telerobotics 1

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丛书名： Springer Tracts in Advanced Robotics

2007年

作者： Manuel Ferre Rafael Aracil Martin Buss Claudio Melchiorri Carlos Balaguer

ISBN: (数字)9783540713647

ISBN: (纸本)9783540713630;9783642425929

At the dawn of the new millennium, robotics is undergoing a major transformation in scope and dimension. From a largely dominant industrial focus, robotics is rapidly expanding into the challenges of unstructured environments. Interacting with, assi- ing, serving, and exploring with humans, the emerging robots will increasingly touch people and their lives. The goal of the new series of Springer Tracts in Advanced Robotics (STAR) is to bring, in a timely fashion, the latest advances and developments in robotics on the basis of their signi cance and quality. It is our hope that the wider dissemination of research developments stimulates exchanges and collaborations among the research community and contributes to further advancement of this rapidly growing eld. The edited volume by Manuel Ferre, Martin Buss, Rafael Aracil, Claudio M- chiorri and Carlos Balaguer is focused on the most recent advances in telerobotics, a technology that deals with the inclusion of a human operator in the control loop of a remote robot. Telerobotics encompasses an area at the crossroads of several scienti c disciplines such as mechatronics, control, communication, computers, sensor-based recognition, multimodality and even teleoperation through Internet.

关键词： control, Robotics, Mechatronics Artificial Intelligence systems Theory, control

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Neuro-fuzzy adaptive control and modeling a thruster of autonomous underwater vehicles

Neuro-fuzzy adaptive control and modeling a thruster of auto...

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作者： Palis, F. Tsepkovskiy, Y. Filaretov, V. Ukhimets, D. Magdeburg University Institute of Electric Energy Systems Universitätplatz 2 / Postfach 4120 39106 Magdeburg Germany Robotic Laboratory Institute for Automation and Control Process Nekrasovskaya str 76 105 Vladivostok 690014 Russia

ISBN: (纸本)9783902661173

The paper presents a direct adaptive fuzzy approach for parameter identification and control of unknown nonlinear systems. To prove the performance of the proposed method an autonomous underwater vehicle (AUV) is modeled using an Adaptive Neuro-Fuzzy Inference System (ANFIS). To guaranty high accuracy the model is adapted online using system input and output data. From the model the current process parameters are identified and utilized for controller design. Here, Single Step Ahead Direct Adaptive control and Multi Steps Ahead Direct control schemes are considered. Copyright © 2006 IFAC.

关键词： Autonomous underwater vehicles

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Improved MRI restoration by integrating Bayesian formalism and support vector machines in a time delayed priors framework

Improved MRI restoration by integrating Bayesian formalism a...

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IST 2006 - 2006 IEEE International Workshop on Imagining systems and Techniques

作者： Karras, D.A. Mertzios, B.G. Chalkis Institute of Technology Dept. Automation Hellenic Open University Rodu 2 Ano Iliupolis Athens 16342 Greece Thessaloniki Institute of Technology Thessaloniki Hellas Democritus University Laboratory of Automatic Control Systems Xanthi Hellas

ISBN: (纸本)1424402514

The goal of this paper is to present the development of a new image restoration methodology for extracting Magnetic Resonance Images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of Support Vector Machine (SVM) models in a time delayed Bayesian priors framework and Bayesian restoration, in the problem of MR image reconstruction from sparsely sampled k-space, following several different sampling schemes, including spiral and radial. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to make measurement time smaller by reducing scanning trajectories as much as possible. In this way, however, underdetermined equations are introduced and poor image extraction follows. It is suggested here that significant improvements could be achieved, concerning quality of the extracted image, by judiciously applying time delayed SVM priors and Bayesian estimation methods to the k-space data. More specifically, it is demonstrated that SVM neural network techniques could construct efficient time delayed Bayesian priors and introduce them in the procedure of Bayesian restoration. These time delayed Bayesian Priors are independent of specific image properties and probability distributions. They are based on training SVM neural filters to estimate the missing samples of complex k-space and thus, to improve k-space information capacity. Such a neural filter based time delayed Bayesian prior is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction. It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the Integrated Bayesian MRI reconstruction approach involving simple and non time delayed SVM models priors [7], by the traditional Bayesian MRI reconstruction approach [1] as well as by the pure Neural Network (NN)

关键词： Image reconstruction

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