基于样本熵的改进小波降噪在微电机质量检测中的应用

微型电机振动信号信噪比低，环境噪声复杂，对噪声信号进行有效去除是对其进行质量检测的关键步骤。针对传统小波降噪阈值函数连续性差、降噪效果不理想等问题，提出一种基于样本熵的改进小波阈值函数，能够根据信号混乱程度自动对阈值函数进行调节。仿真结果表明：在低信噪比环境下，基于样本熵的改进阈值函数降噪效果明显优于传统阈值函数和普通改进阈值函数，信号信噪比得到显著提升。对微型电机异音信号进行降噪处理和特征提取，结合SVM分类器进行训练测试，试验结果表明：改进的小波降噪算法能够有效去除电机信号环境噪声，提取有效的信号特征，对出厂电机性能优劣进行准确判断。该方法将为微型电机厂家大规模质量检测提供理论依据和支持。

Application of Improved Wavelet Denoising Based on Sample Entropy in Quality Inspection of Micromotor

The signal-to-noise ratio of vibration signal of micromotor is low,and the environmental noise is complex.The key step of quality inspection is to remove the noise signal effectively.Aiming at the problems of poor continuity and unsatisfactory denoising effect of traditional wavelet de-noising threshold function,an improved wavelet threshold function based on sample entropy was proposed,which could automatically adjust the threshold function according to the degree of signal confusion.The simulation results show that in the low SNR environment,the improved threshold function based on sample entropy is better than the traditional threshold function and the ordinary improved threshold function,and the signal-to-noise ratio is significantly improved.The noise reduction and feature extraction of the abnormal sound signal of the micromotor were carried out,and the training test was carried out combined with the SVM classifier.The experimental results show that the improved wavelet de-noising algorithm can be used to effectively remove the environmental noise of the motor signal,to extract the effective signal features,so the performance of the motor can be accurately judged.This method will provide theoretical basis and support for large-scale quality inspection of micromotor manufacturers.