中文摘要：

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。

英文摘要：

As the traditional wavelet denoising algorithm based on the soft or hard threshold function cannot effectively eliminate the interference of mill noise, miscalculation of mill load state and low production efficiency happen. To solve the problem, the paper presented a wavelet denoising method based on adaptive threshold function. According to acquired real-time mill sound signal, the parameter values used for the adaptive threshold function were abstracted, and the optimal threshold of signal coefficient was obtained based on SURE non-deviation estimation. Consequently, wavelet adaptive threshold denoising was conducted, thus the achieved mill signal was easier for detection of mill state. After simulation on acquired mill signal on spot, the processed mill sound signal via the approach more accurately reflected the mill load, which possessed significance for improvement of mill productivity and reduction of energy consumption.