Neural network realization of Markov reliability and fault-tolerance models

Suliman, Mamoun; Manzoul, Mahmoud A.

doi:10.1016/0026-2714(91)90358-e

Cited by 8 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…for estimating parameters of distributions [1], and in combination with model-based methods, e.g. to accelerate optimization computations for very complex reliability block diagrams [48], or to determine the transition probability in homogenous Markov processes [49].…”

Section: Introductionmentioning

confidence: 99%

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

Fink

Zio

Weidmann

2014

Reliability Engineering & System Safety

103

View full text Add to dashboard Cite

. Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering and System Safety, Elsevier, 2014, 121, pp.198-206 AbstractIn 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. MLMVN is applied to a case study of predicting the level of degradation of railway track turnouts using real data. The performance of the algorithms is first evaluated using benchmark study data. The results obtained in the reliability prediction study of the benchmark data show that MLMVN outperforms other machine learning algorithms in terms of prediction precision and is also able to perform multi-step ahead predictions, as opposed to the previously best performing benchmark studies which only performed up to two-step ahead predictions. For the railway turnout application, MLMVN confirm the good performance in the long-term prediction of degradation and does not show accumulating errors for multi-step ahead predictions.

show abstract

Section: Introductionmentioning

confidence: 99%