The health management of railway vehicles is crucial to secure safety and efficiency in the long-term operation of high-speed trains. Meanwhile, complex components put forward a higher requirement for the robustness of condition monitoring systems, especially abilities to identify unexpected faults. The misidentification of infrequent faults could lead to unpredictable consequences for the vehicle's safety. This paper proposes a novel method for detecting unexpected faults of high-speed train bogie based on Bayesian deep learning. First, a Monte Carlo-Based perturbation is imposed on input samples, which can magnify the difference between unexpected faults and known ones. Then, through dropout-based Bayesian deep learning, the diagnosis result can be obtained as well as a Bayesian indicator of whether the anomalies belong to known classes. The proposed method can capture the uncertainty of model outputs and identify unexpected faults, requiring only a few samples of unexpected anomalies for calibration. Also, it is compatible with most existing neural network structures. The experiments compare the proposed method with existing methods on two real-world applications, which demonstrates the effectiveness and superiority of the proposed scheme.

中文翻译：

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基于贝叶斯深度学习的高速列车转向架意外故障检测


铁路车辆的健康管理对于保障高速列车长期运行的安全性和高效性至关重要。同时，复杂的部件对状态监测系统的鲁棒性，尤其是意外故障的识别能力提出了更高的要求。对不常见故障的错误识别可能会给车辆安全带来不可预测的后果。本文提出了一种基于贝叶斯深度学习的高速列车转向架意外故障检测新方法。首先，对输入样本施加基于蒙特卡罗的扰动，这可以放大意外故障与已知故障之间的差异。然后，通过基于dropout的贝叶斯深度学习，可以获得诊断结果以及异常是否属于已知类别的贝叶斯指标。所提出的方法可以捕获模型输出的不确定性并识别意外故障，仅需要少量意外异常样本进行校准。此外，它与大多数现有的神经网络结构兼容。实验将所提出的方法与现有方法在两个实际应用中进行了比较，证明了所提出方案的有效性和优越性。