Journal ArticleDOI
Elman's recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery
TLDR
The high performance of Elman's RNN was shown by means of two different applications: detecting anomalies introduced from the simulated power operation of a high-temperature gas cooled nuclear reactor and detecting motor bearing damage using a coherence function approach for induction motors.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2003-10-01. It has received 110 citations till now. The article focuses on the topics: Condition monitoring & Recurrent neural network.read more
Citations
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Journal ArticleDOI
Recurrent neural network and a hybrid model for prediction of stock returns
TL;DR: A novel hybrid model is proposed for prediction of stocks returns which is hybrid of two linear models and a non-linear model which outperforms recurrent neural network.
Journal ArticleDOI
Diagnosis of polymer electrolyte fuel cells failure modes (flooding & drying out) by neural networks modeling
TL;DR: This paper presents a diagnosis procedure of water management issues in fuel cell, namely flooding and drying out, based on a limited number of parameters that are, besides, easy-to-monitor.
Journal ArticleDOI
Forecasting nonlinear time series with a hybrid methodology
TL;DR: The proposed hybrid approach combining Elman's Recurrent Neural Networks (ERNN) and ARIMA models is applied to Canadian Lynx data and it is found that the proposed approach has the best forecasting accuracy.
Journal ArticleDOI
Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China
TL;DR: A comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes.
Journal ArticleDOI
Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring
TL;DR: A new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN), which shows the advantage of the proposed method in adaptive features selection and superior accuracy in Bearing condition recognition.
References
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Journal ArticleDOI
Finding Structure in Time
TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
Proceedings ArticleDOI
Motor bearing damage detection using stator current monitoring
TL;DR: In this article, the authors used motor current spectral analysis to detect rolling-element bearing damage in induction machines, where the bearing failure modes were reviewed and bearing frequencies associated with the physical construction of the bearings were defined.
Book
Advanced Signal Processing and Digital Noise Reduction
TL;DR: Introduction to Signal Processing and Noise Reduction Stochastic Processes and Statistical Characterization of Signals Signal Transforms Bayesian Probabilistic Estimation Theory Wiener Filters and Kalman Filters Linear Prediction Models.
Proceedings ArticleDOI
Shaft voltages and rotating machinery
TL;DR: In this paper, the four distinct types of shaft current damage, frosting, spark tracks, pitting, and welding are described, and methods for correction and/or elimination of these sources are reviewed, so that users can better understand how these voltages and currents are generated.
Journal ArticleDOI
Real-time nuclear power plant monitoring with neural network
TL;DR: A new learning technique adopted here compensates for the drawback of the conventional backpropagation algorithm, and is presented to make plant dynamic models on the ANN to detect anomalies of nuclear power plants in operation.