自适应阈值函数小波算法的电机振动信号去噪

通常采集的电机振动信号中都含有噪声成分,一般采用小波阈值去噪处理可以达到理想的效果,但是传统软、硬阈值函数存在恒定偏差或不连续等缺点,不能很好的保留有用信息。针对这一问题,文中提出了随分解层数自适应且具有调整参数的阈值函数。改进的小波阈值函数连续、可微且具有渐进性,通过遗传算法做调整参数寻优,在保留大部分原始信号的基础上进行去噪,使得新的小波去噪算法在保留有用信息与去除噪声之间有较好的平衡性。实验中将该方法应用于所采集的电机振动信号,结果显示,该方法具有更高的信噪比与较低的均方根误差,能够更好的滤除噪声,保留原信号的有用信息。

Motor Vibration Signal Denoising of Adaptive Threshold Function Wavelet Algorithm

Generally, the vibration signal of the collected motor contains noise components. The wavelet threshold denoising process can achieve good results. However, the traditional soft and hard threshold functions have the disadvantages of constant deviation or discontinuity, and the useful information cannot be well preserved. Aiming at this problem, a new threshold function with adaptive layer number and adjustment parameters was proposed. The improved wavelet threshold function was continuous, differentiable and gradual. The genetic algorithm was used to adjust the parameters and optimize the denoising based on the majority of the original signals, so that the new wavelet denoising algorithm retains useful information and removes noise. There was a good balance between the two. The method was applied to the collected motor vibration signal by experiments. The results showed that the method had higher signal-to-noise ratio and lower root mean square error, which could better filter out noise and retain useful information of the original signal.