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针对传统基于软阈值和硬阈值函数的小波去噪方法不能有效消除磨机干扰噪声,导致磨机负荷状态的误判,从而造成生产效率低这一问题,提出了一种基于自适应阈值函数的小波去噪方法。依据实时采集的磨音信号,提取出自适应阈值函数所需参数值,然后基于SURE无偏估计得到信号系数的最优阈值,再进行小波自适应阈值去噪,得到的磨音信号更易于磨机状态的检测。通过对现场采集的磨音信号进行仿真测试得知,经该方法处理后的磨音信号能更加精确地反映磨机负荷,对提高磨机生产效率和节能降耗具有重要意义。
Aiming at the problem that traditional wavelet denoising method based on soft threshold and hard threshold function can not effectively eliminate mill interference noise and lead to misjudgment of load status of mill, resulting in low production efficiency, an adaptive threshold function Wavelet denoising method. According to the real-time acquisition of the grinding signal, the adaptive threshold function is extracted, and then the optimal threshold of the signal coefficient is obtained based on the SURE unbiased estimation. Then the adaptive threshold of the wavelet is used to denoise the resulting signal. Status detection. Through the simulation test of the grinding sound signal collected in the field, it can be seen that the grinding sound signal processed by the method can more accurately reflect the load of the grinding machine, which is of great significance for improving the mill production efficiency and saving energy and reducing consumption.