Displaying similar documents to “An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection”

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...

Advances in model-based fault diagnosis with evolutionary algorithms and neural networks

Marcin Witczak (2006)

International Journal of Applied Mathematics and Computer Science

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Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding the application of evolutionary algorithms...

Acoustic analysis assessment in speech pathology detection

Daria Panek, Andrzej Skalski, Janusz Gajda, Ryszard Tadeusiewicz (2015)

International Journal of Applied Mathematics and Computer Science

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Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection (hyperfunctional...

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 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...

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...

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...

Note onset detection in musical signals via neural-network-based multi-ODF fusion

Bartłomiej Stasiak, Jędrzej Mońko, Adam Niewiadomski (2016)

International Journal of Applied Mathematics and Computer Science

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The problem of note onset detection in musical signals is considered. The proposed solution is based on known approaches in which an onset detection function is defined on the basis of spectral characteristics of audio data. In our approach, several onset detection functions are used simultaneously to form an input vector for a multi-layer non-linear perceptron, which learns to detect onsets in the training data. This is in contrast to standard methods based on thresholding the onset...