变速箱齿轮磨损故障的极坐标角-频表示与诊断

摘要

变速箱齿轮磨损将导致振动信号中出现冲击响应成分,通过对每转内冲击响应成分的监测,可实现变速箱齿轮磨损故障诊断.为了提高变速箱齿轮磨损故障可视化监测与诊断效果,该文提出了一种极坐标角频分布方法.将采集的变速箱振动信号通过连续小波变换进行消噪处理并转变为极坐标角频分布,充分表现变速箱齿轮不同磨损工况时冲击成分的变化.以每种磨损工况时6转内的能量作为齿轮磨损特征向量,并将特征向量输入给BP神经网络进行分类训练和模式识别,有效地识别了变速箱的4种磨损状态.该研究结果为极坐标角频分布方法在变速箱状态监测与故障诊断的工程应用提供了参考.%The gearbox gear wear tend to result in impulse response in vibration signals, and monitoring impulse response in the rotation cycle, which can achieve fault diagnosis. In order to improve monitoring and diagnosis effects by the visualization, distribution method of the polar digram angle frequency(DPDAF) was introduced in the study. Gearbox vibration signals were denoised by continuous wavelet transform, and then transformed into DPDAF, which can clearly exhibite the impulse response signal differences with the six rotation cycles in the different wear conditions. Six rotation cycle energy were extracted as the feature vectors of gearbox gear wear fault, which were used to train BP neural network for fault pattern recognition. Test results showed that applying DPDAF and BP neural network to gearbox gear wear fault diagnosis was feasible and effective. The results provide a reference for the engineering applications of the polar angle frequency representation in the gearbox condition monitoring and fault diagnosis.