基于性能退化的高铁牵引系统的剩余寿命预测

Remaining Useful Life Prediction of High-speed Railway Traction System Based on Performance Degradation

  • 摘要: 针对高铁牵引系统结构复杂、退化参数较多、失效阈值难以确定等问题,提出了一种基于性能退化的剩余寿命预测方法.首先,提取退化参数的多种统计特征,通过单调性指标、相关性指标、冗余性指标进行特征选择,减少冗余、无关特征的干扰;在缺少失效阈值的情况下,提出一种基于健康状态的策略,通过评估系统的健康状态,判断系统是否失效;然后利用选择的特征训练长短期记忆网络进行退化轨迹预测;最后,在高铁牵引系统半物理实验平台上以中间电路电容性能退化为案例进行算法有效性的验证,结果表明该方法优于现有方法.

     

    Abstract: To address the problems of complex structures, complicated performance degradation parameters, and missing failure threshold, we propose a remaining useful life (RUL) prediction method based on performance degradation. First, we extract various statistical features of degraded parameters, and perform feature selection using monotonic, related, and redundancy indexes to reduce the interference of redundant and irrelevant features. We propose a health-based strategy that assesses the system failure condition by assessing the health of the system to predict RUL without a failure threshold. Then, we use the selected features to train long- and short-term memory networks for degraded trajectory prediction. We conduct a case study using a hardware-in-the-loop simulation platform for the traction system of Chinese railway high-speed trains to predict the RUL of the DC-link circuit with capacitance degradation. Experimental results show the validity and superiority of the proposed method.

     

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