Failure and Reliability Predictions by Infinite Impulse Response Locally Recurrent Neural Networks

Abstract : In this paper, Infinite Impulse Response Locally Recurrent Neural Networks (IIR-LRNNs) are employed for forecasting failures and predicting the reliability of engineered components and systems. To theauthors' knowledge, it is the first time that such dynamic modelling technique is used in reliabilityprediction tasks. The method is compared to the radial basis function (RBF), the traditional multilayerperceptron (MLP) model (i.e., the traditional Artificial Neural Network model) and the Box-Jenkinsautoregressive-integrated-moving average (ARIMA). The comparison, made on case studiesconcerning engine systems, shows the superiority of the IIR-LRNN with respect to both the RBF andthe ARIMA models, whereas a similar performance is obtained by the MLP.
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Conference papers
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https://hal-supelec.archives-ouvertes.fr/hal-00777485
Contributor : Yanfu Li <>
Submitted on : Thursday, January 17, 2013 - 3:41:41 PM
Last modification on : Tuesday, August 13, 2019 - 11:10:04 AM

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  • HAL Id : hal-00777485, version 1

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Enrico Zio, Nicola Pedroni, L. R. Golea. Failure and Reliability Predictions by Infinite Impulse Response Locally Recurrent Neural Networks. CISAP-5, Jun 2012, Milan, Italy. pp.117-122. ⟨hal-00777485⟩

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