基于LabVIEW的齿轮故障智能诊断系统

Intelligent Diagnosis System for Gear Faults Based on LabVIEW

利用Lab VIEW平台开发了齿轮故障诊断系统,系统主要采用共振解调诊断和BP神经网络诊断两种方法。共振解调诊断由Hilbert解调和小波包解调实现故障频率识别;神经网络诊断由对有量纲、无量纲参量提取的特征和根据小波包相对能量提取的特征作为神经网络的输入向量,以齿轮的故障类型作为输出向量,采用BP神经网络对齿轮进行诊断。实验结果表明:通过引入时频分析方法,故障频率检测精度高,故障类型识别准确率较高。

Making use of LabVIEW platform, a diagnosis system for gear faults was developed, whieh adopts resonance demodulation diagnosis and BP neural network diagnosis. Both Hilbert demodulation and wavelet packet demodulation in the resonance demodulation diagnosis can recognize the fault frequency; and through taking the characteristics extracted from both dimension and dimensionless parameters and that from the relative wavelet packet energy as the neural network＇ s input vector and the gear failure type as the output vector, the BP neural network can complete fauh diagnosis. The experimental results show that the introduced time-frequency analysis method can improve both precision and accuracy of detecting the fault frequeney and the fault type.