基于最小熵解卷积与谱峭度融合的变速器轴承故障诊断

Study of Gear Box Bearing Fault Diagnosis Based on Fusion of MED and Spectral Kurtosis

变速器轴承故障是变速器疲劳失效中的重要因素,其直接影响到车辆驾驶感受和行车安全。因此,对轴承故障诊断显得尤为重要,考虑到共振解调方法中带通滤波器的参数选择问题,利用谱峭度方法选择最优滤波频带,将谱峭度方法与MED相结合的故障特征提取方法,研究表明,该方法可以准确判断变速器轴承失效的位置。

Bearing fault diagnosis is very important because gear box bearing fault, as a key factor to cause the transmission fatigue failure, will affect driving experience and safety directly. As a combination of the spectrum kurtosis method with the MED,a fault feature extraction method is used, with the spectral kurtosis to choose the optimal filtering band, in consideration of the pa-rameter selection of bandpass filter with the resonance demodulation method. The research shows that this method can accurately determine the failure position of gear box bearing.