机械

多边形故障作为车轮常见的故障形式之一,不仅会增大列车的振动和噪声、降低列车乘坐舒适性,还会加剧轮轨相互作用力,导致车辆和轨道部件过早出现疲劳失效,对列车安全稳定运行造成不良影响,因此对车轮多边形故障进行诊断具有重要意义。本文根据多边形故障轴箱振动响应提出了基于总体经验模态分解(EEMD)的车轮多边形故障诊断方法。其核心是对轴箱振动加速度进行EEMD分解,然后通过相关能量(CN)自动提取车轮多边形故障的IMF分量,并通过包络谱分析诊断车轮是否存在多边形故障,最后通过频谱分析诊断车轮多边形阶次。通过仿真数据和线路试验数据对该方法进行验证,验证结果表明,该方法能有效诊断出车轮多边形故障。

As one of the common failure forms of train wheels, polygonal fault not only increase the vibration and noise of the railway vehicle, reduce the ride comfort of the railway vehicle, but also enhance the force between the wheel and the rail, which leads to premature fatigue and failure of the vehicle and track components, thus affect the safe and stable operation of railway vehicles. Therefore, it is of great significance to diagnose the polygonal fault of the wheel. According to the vibration response of the axle box of the polygonal wheel, a fault diagnosis method of polygonal wheel based on EEMD is proposed in this paper. The core of the proposed method is to perform EEMD decomposition of the axle box vibration acceleration, and then automatically extract the IMF component of the polygonal fault through the correlated energy(CN), and next diagnose the wheel polygonal fault through the envelope spectrum analysis, and finally determine the polygon order through the spectrum analysis. Simulation test and line test are conducted to verify the effectiveness of the proposed method, which proves that the proposed method can effectively diagnose the wheel polygonal fault effectively.