Results 11 - 20 of 1,124

ADVANCES IN MODEL–BASED FAULT DIAGNOSIS WITH EVOLUTIONARY ALGORITHMS AND NEURAL NETWORKS

by Marcin Witczak
"... 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 ..."
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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 and neural networks to fault diagnosis. In particular, a brief introduction to these computational intelligence

TOWARDS ROBUSTNESS IN NEURAL NETWORK BASED FAULT DIAGNOSIS

by Krzysztof Patan, Marcin Witczak, Józef Korbicz
"... 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 industr ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
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 scheme with respect to modelling uncertainty, two different neural network based schemes are described

Multiscale Convolutional Neural Networks for Vision–Based Classification of Cells

by Pierre Buyssens, Abderrahim Elmoataz, Olivier Lézoray, Université De Caen
"... Abstract. We present a Multiscale Convolutional Neural Network (MCNN) approach for vision–based classification of cells. Based on several deep Convolutional Neural Networks (CNN) acting at different resolutions, the proposed architecture avoid the classical handcrafted features extraction step, by p ..."
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Abstract. We present a Multiscale Convolutional Neural Network (MCNN) approach for vision–based classification of cells. Based on several deep Convolutional Neural Networks (CNN) acting at different resolutions, the proposed architecture avoid the classical handcrafted features extraction step

Comparing the Fault Diagnosis Performances of Single Neural Networks and Two Ensemble Neural Networks Based on the Boosting Methods

by Pooria Karimi, Hooshang Jazayeri-rad , 2014
"... particular the feed-forward multilayer structure, to achieve fault diagnosis in chemical plants. Artificial neural networks can automatically store information by learning from historical fault data without using any qualitative or quantitative model of the system. However, different limitations are ..."
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are encountered in utilizing a single neural network classifier in fault diagnosis of complex large-scale chemical processes. These limitations are mostly originated from the low reliability and also high generalization error of a single neural network classifier which may be faced in real applications

Bearing Fault Diagnosis based on Neural Network Classification and Wavelet Transform

by Omar José, Lara Castro, Cristina Castejón Sisamón, Juan Carlos, García Prada
"... Abstract:- Automated fault classification has been an important pattern recognition problem for decades. In the performance of all motor driven systems, bearings play an important role. The purpose of condition monitoring and fault diagnostics are to detect and distinguish faults occurring in machin ..."
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.The results demonstrate that the developed diagnostic method can reliably detect and classify four different bearing fault conditions into distinct groups. Key-Words:- Wavelets, Artificial networks, Fault diagnosis, Signal processing, Pattern classification 1

Deep Convolutional Neural Network for Image

by Li Xu, Jimmy Sj. Ren, Ce Liu, Jiaya Jia
"... Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an ideal linear convolution model due to camera noise, saturation, image compression, to name a few. Instead of perfectly modeling outliers, which is rather challenging from a generati ..."
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generative model perspec-tive, we develop a deep convolutional neural network to capture the characteristics of degradation. We note directly applying existing deep neural networks does not produce reasonable results. Our solution is to establish the connection between traditional optimization-based schemes

Neural Network Based Algorithm of Soft Fault Diagnosis in Analog Electronic Circuits

by Doried Mismar, Ayman Abubaker , 2010
"... This paper addresses the fault diagnosis in analog electronic circuits based on neural network. Iterative algorithm of solving the system of diagnostic equations, using integral sensitivity matrix is also proposed in this paper. To obtain the system of diagnostic equations, the integral sensitivity ..."
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This paper addresses the fault diagnosis in analog electronic circuits based on neural network. Iterative algorithm of solving the system of diagnostic equations, using integral sensitivity matrix is also proposed in this paper. To obtain the system of diagnostic equations, the integral sensitivity

Model-Based-Diagnosis for Fault Management in telecommunications Networks

by Aomar Osmani
"... Fault management is a crucial problem for telecommunica-tion networks. The network complexity requires articial intelligence techniques to assist the operators in supervision tasks. Initially expert systems techniques were proposed, presently various techniques are used: neural networks, constraint ..."
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Fault management is a crucial problem for telecommunica-tion networks. The network complexity requires articial intelligence techniques to assist the operators in supervision tasks. Initially expert systems techniques were proposed, presently various techniques are used: neural networks, constraint

Model-based fault detection and isolation method using ART2 neural network

by I. S. Lee, J. T. Kim, J. W. Lee, D. Y. Lee, K. Y. Kim - International Journal of Intelligent Systems
"... This article presents a model-based fault diagnosis method to detect and isolate faults in the robot arm control system. The proposed algorithm is composed functionally of three main parts: parameter estimation, fault detection, and isolation. When a change in the system occurs, the errors between t ..."
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This article presents a model-based fault diagnosis method to detect and isolate faults in the robot arm control system. The proposed algorithm is composed functionally of three main parts: parameter estimation, fault detection, and isolation. When a change in the system occurs, the errors between

Rolling Bearing Diagnosis Based on LMD and Neural Network

by Baoshan Huang, Wei Xu, Xinfeng Zou
"... Inner ring pitting, the outer indentation and rolling element wear are typical faults of rolling bearing. In order to diagnose these faults rapidly and accurately, the paper proposes a novel diagnosis method of rolling bearing based on the energy characteristics of PF component and neural network by ..."
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Inner ring pitting, the outer indentation and rolling element wear are typical faults of rolling bearing. In order to diagnose these faults rapidly and accurately, the paper proposes a novel diagnosis method of rolling bearing based on the energy characteristics of PF component and neural network
Results 11 - 20 of 1,124