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ON THE APPLICABILITY OF MODEL-BASED FAULT-DETECTION FOR TECHNICAL processES

control engineering PRACTICE 1994年第3期2卷 439-450页

作者： ISERMANN, R Institute of Automatic Control Laboratory for Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg-Straβe 4 D-64283 Darmstadt Germany

Beginning with classical supervision a summary is given of the main principles of model-based fault detection methods. Their basic assumptions, results from simulation and experiments allow a discussion of the features of the methods with regard to their applicability. The importance of realistic fault modeling is outlined. Finally the role of model-based fault detection as one information source for fault diagnosis is discussed. Several methods of fault detection have to be integrated to support a powerful diagnosis.

关键词： FAULT DETECTION SUPERVISION MONITORING PARAMETER ESTIMATION STATE ESTIMATION PARITY EQUATIONS TECHNICAL processES ACTUATORS SENSORS FAULT DIAGNOSIS

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SELFTUNING OF CLASSICAL controlLERS WITH FUZZY-LOGIC

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MATHEMATICS AND COMPUTERS IN SIMULATION 1994年第2-3期37卷 101-110页

作者： PFEIFFER, BM ISERMANN, R Institute of Automatic Control Laboratory of Control Engineering and Process Automation Darmstadt Technical University Landgraf-Georg Str. 4 D-64283 Darmstadt Germany

Selftuning of classical PI-controllers for processes without known mathematical models is achieved by applying heuristic fuzzy rules. Step changes of the reference input are used to assess control performance in terms of overshoot, rise time and settling time of the step response. Similar to the strategy of an experienced plant operator, a fuzzy logic ''supervision and tuning module'' can conclude from these features, which changes of the controller parameters will improve control behaviour. The concept was tested in simulation series with Matlab/Simulink. Results show the benefits of the proposed fuzzy tuning module applied to 8 different simulated test plants. Experiments with speed-regulation of a DC-servomotor indicate that the concept is suitable for processes with noisy signals as well.

关键词： Self tuning control systems

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Symbolic analysis and synthesis of linguistic parametrizations in fuzzy controllers

Symbolic analysis and synthesis of linguistic parametrizatio...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： B.-M. Pfeiffer R. Isermann Laboratory of Control Engineering and Process Automation Darmstadt University of Technology Darmstadt Germany

Analytical formulas for the transfer characteristic of several SISO fuzzy controllers are developed using a computer algebra system. Different formulations of the linguistic output variables and different inference methods can be compared with respect to their impact on control characteristics. The offline-defuzzyfication strategy resulting in singletons as output attributes already frequently used in practice is theoretically supported. The example of a fuzzy PI controller with two input variables show the application of the proposed method to the systematic design of fuzzy controllers.< >

关键词： Fuzzy control Automatic control Fuzzy systems Input variables control systems Inference algorithms Fuzzy sets Gain Gravity Laboratories

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Fault Diagnosis with Dynamic Neural Structure and Application to a Turbocharger

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IFAC Proceedings Volumes 1994年第5期27卷 597-602页

作者： M. Ayoubi Technical University of Darmstadt Inst. of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg 4 64283 Darmstadt Germany

An attempt has been made to establish a nonlinear dynamic time discrete neuron model, the so called Dynamic Elementary processor (DEP). This DEP disposes of local memory, in that it has dynamic states. Based on the DEP neuron, a dynamic Multi Layer Perceptron neural net (MLP) is proposed to identify nonparametric, multi-input single-output (MISO) models for nonlinear, dynamic systems. The identified models are used to build a bank similar to observer based schemes. The output residuals between the process and the bank models are used to detect and identify a fault in the process, if it has occurred. An empirical MISO model for the turbine of a turbosupercharger was identified to demonstrate the identification ability of the proposed DEP net with real data. The fault detection scheme was successfully applied to detect and diagnose a transient fault in the turbine waste gate.

关键词： Dynamic neuron model nonlinear system identification dynamic error backpropagation turbosupercharger Diesel engine dynamic fault detection fault diagnosis

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Model based supervision of lateral vehicle dynamics

Model based supervision of lateral vehicle dynamics

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American control Conference (ACC)

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

An approach for supervision of vehicle dynamics is presented which may be used for intelligent vehicle control and state monitoring. In particular, an on-board process parameter estimation was implemented which allows one to compute the physical coefficients of lateral vehicle models and their changes during operation. In addition, tire- and velocity-dependent look-up tables in presently used vehicle models were replaced by feedforward neural networks. In the phase of driving state monitoring, a set of these hybrid models-each of them trained for a special driving situation-predict the vehicle motion as a result of the actual steering angle and velocity. In a further step, suitable classification algorithms were used to detect the actual driving state by processing the residual output.

关键词： Vehicle dynamics Vehicle driving Parameter estimation Monitoring Road vehicles Automatic control Least squares approximation Character generation Acceleration Laboratories

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Estimation of parity equations in nonlinear systems

Estimation of parity equations in nonlinear systems

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IEEE Conference on control Technology and Applications (CCTA)

作者： Hofling Deibert Laboratory of Control Engineering and Process Automation Institute of Automatic Control Technical University of Darmstadt Darmstadt Germany

Many processes with nonlinear behaviour are difficult to model. Consequently model based approaches to fault detection can not be applied. For a certain class of these processes with mainly constant input signals a combination of parameter estimation techniques and the parity space approach allows online fault detection with small signal models. Only few knowledge of the process is required: An idea about the dynamic model order and the static nonlinear characteristic curve. Using the proposed method fault detection is performed at a flowrate control with a pneumatic driven valve.< >

关键词： Nonlinear systems Failure analysis Parameter estimation

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Nonlinear dynamic systems identification with dynamic neural networks for fault diagnosis in technical processes

Nonlinear dynamic systems identification with dynamic neural...

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IEEE International Conference on Systems, Man and Cybernetics

作者： M. Ayoubi Laboratoty of Control Engineering and Process Automation Institute of Automatic Control Technical University of Darmstadt Darmstadt Germany

Based on the dynamic neuron model-the so called dynamic elementary processor-a dynamic multilayer perceptron neural net (DMLP) is applied to identify black box models of the process. The dynamic adaption algorithm is briefly introduced and compared to other adaption procedures. However, the identified models are used to build the first step of a fault diagnosis scheme (FDS) similar to observer based schemes. The residuals between the measured process output and the outputs estimated by the bank models are used as numerical symptoms for the fault detection and diagnosis. The FDS was successfully applied to monitor the turbine state of a turbosupercharger.< >

关键词： System identification Fault diagnosis Neurons Multilayer perceptrons Neural networks Multi-layer neural network Heuristic algorithms Observers Fault detection Monitoring

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Modelling and control of longitudinal vehicle motion

Modelling and control of longitudinal vehicle motion

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American control Conference (ACC)

作者： S. Germann R. Isermann Laboratory for Control Engineering and Process Automation Institute of Automatic Control Technical University of Darmstadt Darmstadt Germany

Discusses model based methods for controlling vehicle dynamics. A nonlinear model describing the longitudinal motion of a vehicle is presented. This model consists of a braking system, a powertrain and mass-damper system with damping from aerodynamical drag and rolling resistance. According to the most essential time constants, parameter estimation is used to determine the mass and the aerodynamical drag of the vehicle. As an example for controller design, a combination of fuzzy logic and a classical feedback linearization is presented. Furthermore, some examples of a velocity controller are shown using the described controlling techniques.

关键词： Motion control Power system modeling Drag Velocity control Vehicle dynamics Mechanical power transmission Damping Parameter estimation Linear feedback control systems Fuzzy logic

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Industrial Actuator Benchmark Test - A Systematic Approach To An Optimal FDI

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IFAC Proceedings Volumes 1994年第5期27卷 495-500页

作者： Th. Höfling Th. Pfeufer R. Deibert R. Isermann Institute of Automatic Control Laboratory for Control Engineering and Process Automation Technical University Darmstadt Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +*** Fax: +***

In order to apply the best fault detection and diagnosis scheme, it is a must to investigate the process model profoundly and the kinds of faults to be detected. Especially the process excitation and the effect of a considered fault play an important role. This is the starting point for the choice of one of the various model based fault detection methods. According to this strategy two different approaches, an observer based and a signal based approach, are selected for the two given faults of the benchmark task. Moreover it is shown that the application of adaptive thresholds can improve the performance of the fault detection scheme significantly with respect to false alarm rate and delay of detection.

关键词： Fault detection model based observer based adaptive thresholds

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Integration of Fault Detection and Diagnosis Methods

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IFAC Proceedings Volumes 1994年第5期27卷 575-590页

作者： R. Isermann Institute of Automatic Control Laboratory for Control Engineering and Process Automation Technical University Darmstadt Landgraf-Georg-Straße 4 D-64283 Darmstadt F.R. Germany

For the fault detection of technical processes different methods can be applied based on the information extracted from direct measured signals, from signal models and process models. Examples for signal model based fault detection methods are spectral analysis or parameter estimation of ARMA models, examples for process model based methods are parameter estimation, state estimation or parity equation approaches. A comparison of these methods shows that they have different properties with regard to the detection of faults in the process, the actuators and the sensors. After a brief review of model based fault detection methods the various advantages and disadvantages of the different methods are summarized. The importance of realistic fault modeling is outlined and the suitability of model-based fault detection methods in dependence on the type of faults is discussed. By a proper integration of different fault detection methods mainly their advantages can be used and their disadvantages avoided. It will be shown which integrations of fault detection methods are appropriate to generate analytical symptoms which are then treated by methods of change detection and classification. For fault diagnosis a knowledge based procedure is required, because in addition to analytical symptoms also qualitative information in form of heuristic symptoms have to be taken into account. It will be shown how based on heuristic process knowledge as fault-symptom causalities and a unified representation of all symptoms an integrated fault diagnosis can be performed. This comprises the treatment of the symptom as uncertain facts and approximate diagnostic reasoning via if-then rules either in a probabilistic or a fuzzy-logic (possibilistic) frame. As examples for integrated fault detection and diagnosis experimental results are shown which were obtained with machine tools. In summary, the contribution shows how through proper integration of different fault detection methods and integration

关键词： Fault detection supervision monitoring parameter estimation state estimation parity equations technical processes actuators sensors fault diagnosis diagnostic reasoning fuzzy reasoning machine tools drilling d.c.motor

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