Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7195879 | Reliability Engineering & System Safety | 2014 | 29 Pages |
Abstract
MLMVN are 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 outperform other machine learning algorithms in terms of prediction precision and are 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 do not show accumulating errors for multi-step ahead predictions.
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Authors
Olga Fink, Enrico Zio, Ulrich Weidmann,