说明：  对重分配小波尺度谱存在着时、频分辨率不能同时达到最佳及当振动信号中存在着能量较大的噪声时会降低其时频分布可读性的缺陷，提出一种基于参数优化和奇异值分解(SVD)提高重分配尺度谱时频分布可读性的方法。首先利用Shan— non熵方法优化重分配尺度谱基函数的时间．带宽积(TBP)，克服其时、频分辨率不能同时达到最佳的缺陷，再对重分配尺度谱 进行SVD降噪降低噪声干扰影响，提高时频分布的可读性。最后用该方法对仿真信号和滚动轴承故障信号进行了分析，结果表明该方法的时频聚集性更好，抗噪能力更强，能更有效地识别强噪声背景下的机械故障特征。
(Redistribution wavelet scale spectrum for the existence of time and frequency resolution can not be reached when the vibration signal is the best and the existence of large noise energy will reduce the time-frequency distribution readability defects is proposed based on parameter optimization and Singular value decomposition (SVD) to improve spectrum reallocation time-frequency distribution readability scale approach. Firstly, Shan-non entropy method to optimize re-allocation of the time-scale spectral basis functions. Bandwidth product (TBP), to overcome the time and frequency resolution can not achieve the best defect, and then the re-allocation of spectrum SVD scale noise reduce noise interference and improve the readability of time-frequency distribution. Finally, the method and the rolling bearing fault signal simulated signals were analyzed, the results show that the method of time-frequency aggregation better noise immunity stronger, can more effectively identify strong noise ch)