Hilbert-小波变换的齿轮箱故障诊断*

采用希尔伯特—小波变换对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用小波变换分解获得振动响应信号的各层高频信号小波系数和低频信号小波系数，对小波系数进行重构获得具有不同特征时间尺度的各高频信号和低频信号；再对分解的信号进行希尔伯特变换获得时频信息谱以提取系统的统计特征信息，实现监测齿轮运转工作状态，及时发现齿轮的早期故障，提高机械运行的安全性。仿真研究结果表明，小波变换分解和希尔伯特边际谱方法在故障信息诊断方面是可行和有效的，提高了故障检测的可靠性。

Fault diagnosis of gearbox based on Hilbert and wavelet transform

This paper proposed characteristics analysis of gearbox vibration response signals capture from vibrating acceleration sensor based on Hilbert-wavelet transform (HWT). The vibration response signal was firstly decomposed into high frequency wavelet coefficients and low frequency wavelet coefficients by the wavelet transform method. Then the each high frequency signals and low frequency signal with different time scales characteristics could be obtained by the wavelet coefficients reconstructed. The desired feature of statistical properties of vibration signals could be extracted from time-frequency information spectrum, which could be obtained through Hilbert transform of decomposed signals. It was significant for the mechanical operation security to do some research on how to monitor operating state of gear and detect incipient faults as soon as possible. Experiment results show that the feasibility and efficiency of the wavelet transform and Hilbert marginal spectrum method in fault message diagnosis. Additionally, the algorithm is very reliable to be implemented with fault detection.