【摘要】 基于振动信号的走行部故障诊断是目前列车健康监测的重要途径之一，选择准确的解调频带是故障识别的先决条件。由于列车走行部结构复杂、运行环境恶劣，早期故障信号容易被各种干扰信号遮蔽。针对走行部复杂的振动环境，结合故障识别(fault diagnosis,FD)算法在三分法的基础上引入了循环分量占比(ratio of cyclic content, RCC)，提出了RCCFD算法。通过对仿真信号、滚动台振动信号、列车轴箱振动信号的对比分析，结果表明：RCC-FD算法能够在强循环脉冲干扰、随机脉冲干扰的工况下提取出以目标故障特征信息为主导的最优频带，同时具有更高的计算效率，更加适用于列车走行部实时故障诊断。更多还原

【Abstract】 The fault detection of bogies based on vibration signal is one of the important ways of train health monitoring. It’s a prerequisite for fault identification to select an accurate demodulation frequency band. Due to the complex structure of the train bogies and the bad operation environment, the early fault signal is easily covered by various interference signals. The RCC-FD algorithm is proposed by combining the FD algorithm with the introduction of RCC based on the trichotomy method. Through the comparative analysis of simulation signals,rolling table vibration signals and axle box vibration signals, the results show that RCC-FD algorithm can extract the optimal frequency band dominated by target fault feature under the condition of strong cyclic pulse interference and random pulse interference. At the same time, RCC-FD algorithm is computationally efficient. It is more suitable for real-time fault diagnosis of train bogies.更多还原