用样条背拟合核平滑的广义加性模型预测高速铁路列车车轮多面化

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI:10.1109/ICPHM.2019.8819407

Zhexiang Chi, Lijian Yang, Jing Lin, Simin Huang

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引用次数: 1

摘要

提出了一种基于广义加性模型的高速铁路列车车轮多面化预测方法。与以往的大多数研究不同，本研究使用了现场数据，因此研究结果有助于提高实际维护效率。在计算模型参数时，采用样条反拟合核，提高了计算效率。该预测方法可应用于实际的铁路管理决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Prognostics of polygonalization of high-speed railway train wheels using a generalized additive model smoothed by spline-backfitted kernel

A method for the prognosis of polygonalization in high-speed railway train wheels is developed based on a generalized additive model. Unlike most previous studies, this study uses field data, so findings can help improve practical maintenance efficiency. A spline-backfitted kernel is used to improve computation efficiency when figuring out model parameters. This prognostics method can be applied to practical railway management decisions.

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来源期刊

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

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