• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Engineering Applications of Artificial Intelligence >An RBMs-BN method to RUL prediction of traction converter of CRH2 trains
【24h】

An RBMs-BN method to RUL prediction of traction converter of CRH2 trains

机译：RBMs-BN方法预测CRH2列车牵引变矩器的RUL

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Remaining useful life (RUL) prediction is essential to ensure safety and reliability of engineering systems. To achieve better prediction performance, causalities among the physical quantities are considered by applying Bayesian Network (BN) to RUL prediction. For this purpose, several improvements on BN modeling are made in this paper, to handle the closed-loop control structure of engineering systems, and to improve prediction performance with reduced complexity. Taking the traction converter of CRH2 trains as the object of the research, a closed-loop Bond Graph (BG) model is firstly developed to describe the causality of multi-domain physical quantities, which is then transformed to be a BN structure. Then, multi-dimensional features are extracted from the condition monitoring data and are used as the inputs to the nodes of BN model. Finally, Restricted Boltzmann Machines (RBMs) are used to further extract the latent features that cannot be directly observed or measured, but greatly improve the accuracy of the BN based RUL prediction. Case study is conducted using a hardware-in-loop simulation platform for traction system of China Railway High-speed (CRH2) trains, to predict RUL of the DC-link circuit with degradation of capacitance or resistance. The experimental results can show the validity and superiority of the proposed RBMs-BN based RUL prediction method.
机译：剩余使用寿命（RUL）预测对于确保工程系统的安全性和可靠性至关重要。为了获得更好的预测性能，通过将贝叶斯网络（BN）应用于RUL预测来考虑物理量之间的因果关系。为此，本文对BN建模进行了一些改进，以处理工程系统的闭环控制结构，并以降低的复杂性提高了预测性能。以CRH2列车的牵引变矩器为研究对象，首先建立了闭环结合图模型，描述了多域物理量的因果关系，然后将其转化为BN结构。然后，从状态监视数据中提取多维特征，并将其用作BN模型节点的输入。最后，使用受限玻尔兹曼机（RBM）进一步提取无法直接观察或测量的潜在特征，但极大地提高了基于BN的RUL预测的准确性。案例研究是使用中铁高速（CRH2）列车牵引系统的半实物仿真平台进行的，以预测直流链电路的RUL随电容或电阻的降低而变化。实验结果可以证明所提出的基于RBMs-BN的RUL预测方法的有效性和优越性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号