CDC2000 Model-based Fault Diagnosis of Industrial Processes

Model-based Fault Diagnosis of Industrial Processes
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation 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Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Identification And Fault Diagnosis Of An Industrial Gas Turbine Prototype Model Authors: Silvio Simani, Ron J. Patton, Steve Daley, Andrew Pike, Volume: 1, Page 2615 Paper number 5801 Abstract: This paper addresses a model-based procedure exploiting analytical redundancy for the detection and isolation of faults of a power plant. The residual generation is performed by means of output observers and Kalman filters in connection with the uncertainty affecting the measurements acquired from the monitored system. The model of the process under investigation required to design observers and filters is obtained by identification The proposed fault detection and isolation tool has been tested on a simulated model of an industrial gas turbine prototype. CD005801.PDF (From Author) TOP Identification And Fault Diagnosis Of Nonlinear Dynamic Processes Using Hybrid Models Authors: Silvio Simani, Cesare Fantuzzi, Sergio Beghelli, Volume: 1, Page 2621 Paper number 5802 Abstract: This work addresses a novel approach for fault diagnosis of industrial processes using hybrid models. A nonlinear dynamic process can, in fact, be described as a composition of different affine submodels selected according to the process operating conditions. This paper concerns the identification of hybrid model parameters through input--output data affected by additive noise. The fault detection scheme adopted to generate residuals uses the estimated hybrid model. In order to show the effectiveness of the developed technique, the results obtained in the fault diagnosis of a real industrial plant are reported CD005802.PDF (From Author) TOP Causal Strategy For Set-Membership Fault Diagnosis Authors: Stéphane Ploix, Sylviane Gentil, Volume: 1, Page 2627 Paper number 5803 Abstract: Set-membership fault detection seems to be a promising approach because it takes into account a priori knowledge on modelling uncertainties and measurement errors. It consists in coherency tests between measurements and models. Nevertheless, a careful diagnostic strategy has to be designed in order to achieve fault diagnosis of complex processes. To achieve the diagnostic procedure, a causal analysis allows to focus the coherency tests on simple models, resulting in a decrease of the amount of computations. This paper will present an application to a nuclear waste treatment plant. The proposed causal strategy for diagnosis will be explained first. The set-membership coherency tests will be justified. Finally, a fault scenario will be presented in order to enlighten the method. CD005803.PDF (From Author) TOP Fault Detection Using Immune-Based Systems and Formal Language Algorithms Authors: João F. Martins, Paulo J. Costa Branco, Armando J. Pires, Joaquim A. Dente, Volume: 1, Page 2633 Paper number 5804 Abstract: This paper describes two approaches for fault detection: an immune- based mechanism and a formal language algorithm. The first one is based on the feature of immune systems in distinguish any foreign cell from the body's own cell. The formal language approach assumes the system as a linguistic source capable of generating a certain language, characterised by a grammar. Each algorithm has particular characteristics, which are analysed in the paper, namely in what cases they can be used with advantage. To test their practicality, both approaches were applied on the problem of fault detection in an induction motor. CD005804.PDF (From Author) TOP Reliability In The Determination Of Gas Turbine Operating State Authors: Pier Ruggero Spina, Volume: 1, Page 2639 Paper number 5805 Abstract: This paper describes the problems which arise using gas turbine Health Monitoring Systems and proposes a method for improving the reliability in gas turbine health determination. This is based in the following main steps: () an analysis is initially performed to evaluate the best measurements/parameters combination, in terms of accuracy in gas turbine health determination. () Once the most appropriate characteristic parameters to evaluate based on the available measurements have been selected, the measurements are processed, using techniques of sensor Fault Detection and Isolation (FDI), before they are used as input by the Health Monitoring Systems. In this manner it is possible to verify that the measurements are not affected from errors due to faulty sensors. (*) If a sensor fault is instead detected and isolated, the faulty sensor is repaired or excluded from the gas turbine diagnosis process, to avoid an incorrect evaluation of the machine health state. CD005805.PDF (From Author) TOP Virtual Sensor for Fault Detection and Isolation in Flight Control Systems - Fuzzy Modeling Approach Authors: Marcel Oosterom, Robert Babuska, Volume: 1, Page 2645 Paper number 5806 Abstract: A virtual sensor for normal acceleration has been developed and implemented in the flight control system of a small commercial aircraft. The inputs of the virtual sensor are the consolidated outputs of dissimilar sensor signals. The virtual sensor is a fuzzy model of the Takagi--Sugeno type and it has been identified from simulated data, using a detailed, realistic Matlab/Simulink model used by the aircraft manufacturer. This virtual sensor can be applied to identify a failed sensor in the case that only two real sensors are available and even to detect a failure of the last available sensor. CD005806.PDF (From Author) TOP