Displaying similar documents to “Advances in model-based fault diagnosis with evolutionary algorithms and neural networks”

Towards robustness in neural network based fault diagnosis

Krzysztof Patan, Marcin Witczak, Józef Korbicz (2008)

International Journal of Applied Mathematics and Computer Science

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Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis...

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

Marcin Mrugalski (2013)

International Journal of Applied Mathematics and Computer Science

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This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a...

Fault location in EHV transmission lines using artificial neural networks

Tahar Bouthiba (2004)

International Journal of Applied Mathematics and Computer Science

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This paper deals with the application of artificial neural networks (ANNs) to fault detection and location in extra high voltage (EHV) transmission lines for high speed protection using terminal line data. The proposed neural fault detector and locator were trained using various sets of data available from a selected power network model and simulating different fault scenarios (fault types, fault locations, fault resistances and fault inception angles) and different power system data...

Nonlinear model predictive control of a boiler unit: A fault tolerant control study

Krzysztof Patan, Józef Korbicz (2012)

International Journal of Applied Mathematics and Computer Science

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This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of...

Data-driven models for fault detection using kernel PCA: A water distribution system case study

Adam Nowicki, Michał Grochowski, Kazimierz Duzinkiewicz (2012)

International Journal of Applied Mathematics and Computer Science

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Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system-the water distribution system of the Chojnice town (Poland). This model is utilised for fault detection with the emphasis on water leakage detection. A systematic description of the system's framework is...

Fault tolerance in networked control systems under intermittent observations

Jean-Philippe Georges, Didier Theilliol, Vincent Cocquempot, Jean-Christophe Ponsart, Christophe Aubrun (2011)

International Journal of Applied Mathematics and Computer Science

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This paper presents an approach to fault tolerant control based on the sensor masking principle in the case of wireless networked control systems. With wireless transmission, packet losses act as sensor faults. In the presence of such faults, the faulty measurements corrupt directly the behaviour of closed-loop systems. Since the controller aims at cancelling the error between the measurement and its reference input, the real outputs will, in such a networked control system, deviate...

Fault diagnosis of networked control systems

Christophe Aubrun, Dominique Sauter, Joseph Yamé (2008)

International Journal of Applied Mathematics and Computer Science

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Networked Control Systems (NCSs) deal with feedback control systems with loops closed via data communication networks. Control over a network has many advantages compared with traditionally controlled systems, such as a lower implementation cost, reduced wiring, simpler installation and maintenance and higher reliability. Nevertheless, the networkinduced delay, packet dropout, asynchronous behavior and other specificities of networks will degrade the performance of closed-loop systems....