频域特征驱动的车辆旋转部件灰色故障诊断

Frequency Domain Feature‑Driven Grey Fault Diagnosis of Vehicle Rotating Components

根据不同故障引起的振动信号频域特征各异的特点,首先,运用调Q小波变换(tunable Q-factor wavelet transform,简称TQWT)的频率响应对车辆旋转部件振动信号或其包络信号进行分析,构建以信号在不同频段各子带能量占比为元素的特征向量;其次,针对灰色接近关联度在处理2组相交的序列时存在的两序列变化趋势不同、原始累差小而导致关联度过大的问题,提出了灰色绝对接近关联度模型;最后,在所构建的频域特征向量驱动下,计算其与标准模式的灰色绝对接近关联度,对车辆关键旋转部件故障状态进行识别。利用所提方法对列车轮对轴承和汽车变速器齿轮箱不同运行状态的振动信号进行分析,结果表明,所提方法能够准确识别车辆旋转部件的运行状态和故障类型,通过对比分析验证了该方法的优越性。

Due to the different frequency characteristics of the vibration signals caused by different faults vary,the tunable Q-factor wavelet transform(TQWT)is adopted to analyze the frequency domain response of the vibration signal and its envelope signal.The energy distribution of different sub-bands is calculated and considered as the element to construct the feature vector.Then,aiming at the problem that the traditional grey close relative model cannot correctly reflect the closing degree,the grey absolute close relative model is proposed.Driven by the obtained feature vector,the fault diagnosis of vehicle rotation components can be performed by calculating the closing degree.The proposed method is used to process the vibration signal of train wheelset bearings and automotive transmission gearbox.The results show that the bearing fault type and gear health condition can be correctly identified by the proposed method,which verifies the superiority of the proposed method.