Predicting component reliability and level of degradation with complex-valued neural networks

Olga Fink, Enrico Zio and Ulrich Weidmann

Reliability Engineering and System Safety, 2014, vol. 121, issue C, 198-206

Abstract: In this paper, multilayer feedforward neural networks based on multi-valued neurons (MLMVN), a specific type of complex valued neural networks, are proposed to be applied to reliability and degradation prediction problems, formulated as time series. MLMVN have demonstrated their ability to extract complex dynamic patterns from time series data for mid- and long-term predictions in several applications and benchmark studies. To the authors' knowledge, it is the first time that MLMVN are applied for reliability and degradation prediction.

Keywords: Neural networks; Complex valued neural networks; Reliability prediction; Level of degradation; Railway turnout system (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:121:y:2014:i:c:p:198-206

DOI: 10.1016/j.ress.2013.08.004

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