万向轴动不平衡检测的改进 DTCWT-SVD 方法

摘要

针对经典小波和双树复小波（Dual-Tree Complex Wavelet Transform，DTCWT）频率泄露和混叠的根本缺陷，提出改进 DTCWT 算法，该算法解决了经典小波存在负频率以及经典小波和 DTCWT 滤波器频率不完全截止问题。将改进 DTCWT 算法和奇异值分解（Singular Value Decomposition，SVD）引入到万向轴动不平衡检测中，该方法的核心是：对万向节安装机座的振动信号进行改进 DTCWT 变换得到不同尺度的分解信号，对低频近似信号进行奇异值分解，以奇异值关键叠层作为奇异值的选择准则对信号进行重构，应用重构信号的傅里叶谱来检测高速列车万向轴的动不平衡。该方法在消除经典小波变换和 DTCWT 频率混叠的同时提高谱线清晰度，凸显故障特征。应用万向轴动不平衡试验数据对该方法进行试验验证，结果表明：改进 DTCWT-SVD 能够很好提取出万向轴动不平衡故障特征频率的基频、倍频，与经典小波、DTCWT、纯改进 DTCWT 相比，该方法在谱的清晰度和故障表征力上得到了显著提高。%A new improved algorithm of the dual tree complex wavelet transform (DTCWT)was proposed aiming at dealing with the leakage frequency and aliasing defect existing in classical wavelet transform and coventional DTCWT.The algorithm solves the problem of negative frequencies existing in classic wavelet and the problem of incomplete cut-off of filter frequency in classical wavelet transform and conventional DTCWT.The improved DTCWT and singular value decomposition (SVD)were introduced in the dynamic imbalance detection of cardan shaft.The vibration signals at the base installed with gimbal were decomposed through the improved DTCWT to get the different scale decomposition signal. The low-frequency approximated signal was decomposed by the SVD and the key singular values were selected to reconstruct the vibration signal based on the key stack of singular values.The fourier spectrum of the reconstructed signal was applied to detect the dynamic imbalance of the cardan shaft.The method can eliminate the defects submerged in unbalanced fault signals and highlight the failure characteristics.The method was verified by test data in the condition of dynamic imbalance.The results show the improved DTCWT-SVD can effectively detect the fundamental frequency and frequency multiplication caused by the dynamic imbalance of cardan shaft and the clarity and failure characterization are significantly improved by using the improved DTCWT-SVD.