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Trends in the Application of Model Based Fault Detection and Diagnosis of Technical processes

IFAC Proceedings Volumes 1996年第1期29卷 6325-6336页

作者： Rolf Isermann Peter Ballé Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany

After a short view on the historical development of model-based fault detection some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated based on the work within the IFAC Technical Committee SAFEprocess. Some basic fault detection and diagnosis methods are briefly considered. Then, an evaluation of publications during the last 5 years shows some trends in the application of model-based fault detection and diagnosis methods.

关键词： Fault detection fault diagnosis supervision actuator faults sensor faults process faults state observer parity space parameter estimation classification diagnostic reasoning

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Robust hybrid control based on inverse fuzzy process models

Robust hybrid control based on inverse fuzzy process models

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： M. Fischer R. Isermann Institute of Automatic Control Laboratory of Control Systems and Process Automation Technical University of Darmstadt Darmstadt Germany

Takagi-Sugeno type fuzzy models are universal approximators for nonlinear dynamic processes. If they are trained to represent the inverse plant characteristics they can be used as feedforward controllers. The achievable control performance strongly depends on the model quality, and the simple inverse model controller does not cope with disturbances and process uncertainty. As a consequence, a hybrid control scheme is proposed which considerably improves the robustness properties. This paper reviews the identification of both forward and inverse fuzzy models. The difficulties accompanying the latter task are discussed. The control scheme based on the idea of disturbance observation is introduced and thoroughly analyzed. Finally, the controller is applied to a cooling blast with nonlinear behavior and variant dynamics.

关键词： Robust control Fuzzy control Inverse problems Fuzzy systems Radial basis function networks Humans System identification Partitioning algorithms Least squares methods Iterative algorithms

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Black box modelling of a structure unknown high efficiency boiler applying neural networks with locally distributed dynamics

Black box modelling of a structure unknown high efficiency b...

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Conference of the North American Fuzzy Information processing Society - NAFIPS

作者： J. Ohl M. Ayoubi M. Kurth Institute of Automatic Control Laboratory of Control Engineering & Process Technical University of Darmstadt Darmstadt Germany

The paper considers the application of neural networks with distributed dynamics to the identification of nonlinear systems. The main intention is to provide a simulation tool for the design of control systems to the development engineer. The identification of the thermal plant is accomplished without any a priori knowledge about the nonlinear structure or the dynamics of the plant. Identification results of two different neural nets are shown and compared.

关键词： Boilers Neural networks Aerodynamics Autoregressive processes Nonlinear dynamical systems Temperature control Heating Neurons Optimal control control systems

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Diagnosis of Different Faults in Milling Using Drive Signals and process Models

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IFAC Proceedings Volumes 1996年第1期29卷 601-606页

作者： H. Konrad Isermann R. Technical University of Darmstadt Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +496151 163927 Fax: +49 6151 293445

A new method of fault detection in milling is described. The method uses exclusively drive signals and is based on models for the feed drive and the milling process. Using parameter estimation features are generated which are independent of cutting conditions. A subsequent classifier evaluates the process state and provides a reliable diagnosis of the milling process.

关键词： Machining Fault Diagnosis Parameter Estimation Classification

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Fault Detection in Milling with Parameter Estimation and Classification Methods

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IFAC Proceedings Volumes 1996年第7期29卷 37-42页

作者： H. Konrad Technical University of Darmstadt Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str.4 D-64283 Darmstadt Germany Phone: +49 6151 163927 Fax: +49 6151 293445

In this paper a new method of fault detection in milling is reported. Based on measured cutting forces, model parameters are estimated for each insert of the milling cutter. Using a classifier, the patterns of these estimated parameters are processed further and the state of the milling process is determined. The method is first tested with simulated data and then verified with measurements on a machining center.

关键词： machine tools cutting failure detection neural networks

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Longitudinal and Lateral control and Supervision of Autonomous Intelligent Vehicles

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IFAC Proceedings Volumes 1996年第1期29卷 7850-7855页

作者： H. Holzmann Ch. Halfmann S. Germann M. Würtenberger R. Isermann Technical University of Darmstadt Institute of Automatic Control Laboratory for Control Engineering and Process Automation Landgraf-Georg-Straβe 4 D-64283 Darmstadt Germany Telephone: +49-6151-167407 Telefax: +49-6151-293604

As individual and commercial traffic flow on roads and highways grows enormously, rhe number of accidents increases as well. Therefore, modern vehicle research is focused on improving driving comfort as well as passengers' safety. Aiming at that, recent advances in control engineering and modern computer technology enable the engineer to design special control and supervision systems supporting the driver. Exemplary, this contribution presents two possible solutions. On the one hand, an Adaptive Cruise control system which assists the driver during highway traffic, whereas a vehicle supervision method is applied to detect critical driving situations and sensor faults.

关键词： Vehicle Modelling Velocity control Distance control Vehicle Dynamics Motion Supervision Sensor Fault Detection

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Supervision of Vehicles' Tire Pressure by Measurement of Body Accelerations

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IFAC Proceedings Volumes 1996年第1期29卷 7708-7713页

作者： Ch. Halfmann H. Holzmann M. Ayoubi R. Isermann Technical University of Darmstadt Institute of Automatic Control Laboratory for Control Engineering and Process Automation Landgraf-Georg-Straβe 4 D-64283 Darmstadt Germany Telephone: 0049-6151-167407 Telefax: 0049-6151-293604

Due to the rising consciousness of safety aspects the supervision of vehicles' tire pressure is a major effort to improve active car safety. Therefore, in this contribution a method for monitoring the tire pressure is presented using body acceleration signals. Analysing the frequency spectrum of the virtual transfer function between the body acceleration at the front and the rear wheel of one side of the vehicle characteristic features are generated. Thereby, external interferences to the spectrum and their influences to the symptoms are discussed. Then, a neuro-fuzzy classification of the characteristics is applied to quantify the tire pressure.

关键词： Supervision of Tire Pressure Virtual Transfer Function Signal processing Spectrum Analysis Neuro-Fuzzy Classification

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Application and comparison of alternative position sensors in high-accuracy control of an X-Y table

Application and comparison of alternative position sensors i...

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International Workshop on Advanced Motion control (AMC)

作者： M. Fischer M. Tomizuka Institute of Automatic Control Laboratory of Control Systems and Process Automation Technical University of Darmstadt Darmstadt Germany Department of Mechanical Engineering University of California Berkeley Berkeley CA USA

High-accuracy positioning is applied in a variety of modern computer-controlled machines. The achievable precision is not only determined by the mechanical properties of the systems but strongly depends on the utilized control algorithms and the quality of the sensor signals. The objective of this paper is to demonstrate how alternative position sensors influence the performance of a robust digital tracking controller consisting of a disturbance observer in the velocity loop, a feedback controller in the position loop, and a zero phase error tracking controller as feedforward controller. Two different sensor systems for an x-y positioning table are considered. While a digital encoder is attached to the actuating motor, a laser interferometer with a significantly higher resolution directly measures the position of the compliantly coupled table. The latter case represents a noncollocated system. After introducing the hardware setup, both the system identification and the controller design are briefly reviewed. The impact of the measurement device on the control performance and the optimal choice of the controller parameters are investigated in extensive experiments.

关键词： Mechanical sensors Velocity control control systems Sensor systems Tracking loops Optimal control Application software Mechanical factors Robust control Digital control

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Model-Based Identification of a Vehicle Suspension Using Parameter Estimation and Neural Networks

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IFAC Proceedings Volumes 1996年第1期29卷 4510-4515页

作者： Thomas Weispfenning Steffen Leonhardt Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49(6151)16-7412 FAX:+49(6151)293604

A two-step scheme for identification of a vehicle suspension is presented which combines parameter estimation and neural networks for approximation. At first, the parameters of the discrete time transfer function are estimated using a RLS-algonthm. These parameters are nonlinear functions of the physical coefficients, but a direct calculation of these is often not possible or leads to large errors due to the nonlinear amplification of noise. Therefore, to approximate the coefficients, a nonlinear mapping using a RBF network is performed. For training of the network and to test generalization abilities, the coefficients of a vehicle suspension were varied. The study shows that an approximation of the physical coefficients by application of the presented scheme is possible. The method was tested by simulated data and measurements from a test rig at the Technical University of Darmstadt.

关键词： identification neural networks parameter estimation vehicle suspension supervision fault diagnosis

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Model-Based and Signal-Based Fault Detection of Diesel Engines with Turbocharger

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IFAC Proceedings Volumes 1996年第1期29卷 7832-7837页

作者： Matthias Schüler Steffen Leonhardt Christof Ludwig Mihiar Ayoubi Rolf Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg Str. 4 D-64283 Darmstadt Germany Phone: +49 (6151) 16-7412 FAX: +49 (6151) 293604

Rising demands in automotive development and strict emission standards enforce the application of modern conrrol and supervision strategies to combustion engines. This contribution shows the shaping and adaption of model based fault detection and direct signal analysis methods when applied to a turbocharged diesel engine. First a real lime supervision of fuel mass and injection angle based on dynamic cylinder pressure measurement is described. This is followed by a method for engine misfire detection using only a low resolution crankshaft speed signal. Then fault detection for a diesel engine turbocharger with nonlinear neural networks is proposed. Finally the results of a diagnosis of multiple faults with a neural network are presented. All methods have been implemented and tested experimentally on a dynamical engine test stand at the Technical University of Darmstadt.

关键词： diesel engine fault detection fault diagnosis neural networks state estimation supervision

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