基于双树复小波变换的轴承故障诊断研究

Research on Bearing Fault Diagnosis Based on Dual-tree Complex Wavelet Transform

提出了一种基于双树复小波变换解调技术的轴承故障诊断新方法。该方法利用双树复小波变换具有近似平移不变性、避免频率混叠和有效降噪的优点,首先对轴承故障振动信号进行双树复小波分解和重构,将振动信号分解成实部和虚部,然后计算振动信号的双树复小波幅值包络和包络谱。齿轮箱轴承故障振动实验信号的分析表明,该方法能在强噪声环境下准确提取轴承故障产生的周期性瞬态冲击信号,能有效消除频率混叠现象和强噪声的影响,能有效识别轴承内圈和外圈故障。

A novel method of bearing fault diagnosis based on demodulation technique of DTCWT. It is demonstrated that the proposed dual-tree complex wavelet transform has better shift invariance, reduced frequency aliasing effect and de-noising ability. The bearing fault vibration signals were firstly decomposed and recostructed using dual-tree complex wavelet transform. Then the real and i maginary parts were obtained and the vibration signals were amplitude demodulated. In the end, the amplitude envelope and wavelet envelope spectrum were computed. Therefore, the characteristics of the bearing faults can be recognized according to the wavelet envelope spectrum. The experimental resuits show that fault diagnosis based on dual-tree complex wavelet transform can diagnose bearing faults effectively under strong noise conditions and reduce spectral aliasing.