As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identification of bogie fault types. However, for application scenarios such as auxiliary maintenance, identifying the performance degradation of bogie is critical in determining a particular maintenance strategy. In this article, by considering the intrinsic link between fault type and performance degradation of bogie, a novel multiple convolutional recurrent neural network (M-CRNN) that consists of two CRNN frameworks is proposed for simultaneous diagnosis of fault type and performance degradation state. Specifically, the CRNN framework 1 is designed to detect the fault types of the bogie. Meanwhile, CRNN framework 2, which is formed by CRNN Framework 1 and an RNN module, is adopted to further extract the features of fault performance degradation. It is worth highlighting that M-CRNN extends the structure of traditional neural networks and makes full use of the temporal correlation of performance degradation and model fault types. The effectiveness of the proposed M-CRNN algorithm is tested via the HST model CRH380A at different running speeds, including 160, 200, and 220 km/h. The overall accuracy of M-CRNN, i.e., the product of the accuracies for identifying the fault types and evaluating the fault performance degradation, is beyond 94.6% in all cases. This clearly demonstrates the potential applicability of the proposed method for multiple fault diagnosis tasks of HST bogie system.

中文翻译：

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用于高速列车转向架故障识别和性能下降评估的多重卷积递归神经网络。

转向架的机械性能作为高速列车（HST）的重要组成部分，直接影响HST的安全性和可靠性。事实是，无论潜在的机械性能下降状态如何，大多数现有的故障诊断方法都只专注于转向架故障类型的识别。但是，对于诸如辅助维护之类的应用场景，确定转向架的性能下降对于确定特定的维护策略至关重要。在本文中，考虑到故障类型与转向架性能退化之间的内在联系，提出了由两个CRNN框架组成的新型多卷积递归神经网络（M-CRNN），用于故障类型和性能退化状态的同时诊断。特别，CRNN框架1旨在检测转向架的故障类型。同时，采用由CRNN框架1和RNN模块组成的CRNN框架2，进一步提取故障性能下降的特征。值得强调的是，M-CRNN扩展了传统神经网络的结构，并充分利用了性能下降和模型故障类型的时间相关性。通过HST模型CRH380A在不同的行驶速度（包括160、200和220 km / h）下测试了所提出的M-CRNN算法的有效性。在所有情况下，M-CRNN的整体准确性（即用于识别故障类型和评估故障性能下降的准确性的乘积）均超过94.6％。