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

机译：使用复值神经网络预测组件的可靠性和退化程度

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摘要

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. 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.
机译：本文提出了一种基于多值神经元（MLMVN）的多层前馈神经网络（MLMVN），它是一种特定类型的复杂值神经网络，将其应用于时间序列的可靠性和退化预测问题。 MLMVN已经证明了他们能够从时间序列数据中提取复杂的动态模式，以便在一些应用和基准研究中进行中长期预测。据作者所知，这是MLMVN首次用于可靠性和降级预测。 MLMVN用于通过实际数据预测铁路道岔的退化程度的案例研究。首先使用基准研究数据评估算法的性能。在基准数据的可靠性预测研究中获得的结果表明，就预测精度而言，MLMVN优于其他机器学习算法，并且还能够执行多步超前预测，这与之前执行情况最好的基准研究相反提前两步预测。对于铁路道岔应用，MLMVN在长期退化预测中确认了良好的性能，并且对于多步超前预测没有显示累积误差。

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