自适应改进双树复小波变换的齿轮箱故障诊断

针对双树复小波变换存在频率混叠以及参数需自定义的缺陷,提出自适应改进双树复小波变换的齿轮箱故障诊断方法。首先,利用双树复小波变换将信号进行分解和单支重构,采用粒子群算法将分解后分量峭度值作为适应度函数,选择双树复小波的最优分解层数;其次,对重构出的低频信号进行频谱分析提取故障特征,将单支重构后的各高频分量进行变分模态分解,通过峭度值获得各高频分量经变分模态分解后的主频率分量信号;最后,分析各主频率分量信号的频谱,识别齿轮箱的故障特征。结果表明,该方法与双树复小波变换和变分模态分解相比,不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且还提高了从强噪声环境中提取瞬态冲击特征的能力。

Gearbox Fault Diagnosis Based on Adaptive Modified Dual-tree Complex Wavelet Transform

In the light of frequency aliasing and parameter custom caused by doubletree complex wavelet transform, a fault diagnosis method of adaptive improved dual-tree complex wavelet transform is proposed. This method integrates dual-tree complex wavelet transform-variational mode decomposition (DTCWT-VMD). First, the signal is decomposed and reconstructed by dual-tree complex wavelet transform. Particle swarm optimization (PSO) is used to determine the component kurtosis value as a fitness function to select the optimal decomposition level of doubletree complex wavelet. Second, the reconstructed low-frequency signal is subjected to spectrum analysis to extract the fault characteristic signal. The high-frequency components are reconstructed by variational mode decomposition, and through the kurtosis value, the main frequency component signal of each high-frequency component decomposed by variational mode is obtained. Finally, the spectrum of the main frequency component signals is analyzed to identify the fault frequency of the gearbox. The experimental results show that the proposed method eliminates frequency aliasing and improves the correctness of signal-to-noise ratio and frequency band selection compared with that process by the dual-tree complex wavelet transform and variational mode decomposition. Besides, it improves the ability to extract transient shock characteristics from a strong noisy environment.