因小波变换具有多尺度分析的特点，在时频两域都有表征信号局部特征的能力<参考文献原文>，因此采用小波分解方法研究了不同时频成分的磁巴克豪森(MBN)信号随温度和应力变化的灵敏度问题。采用db5小波对MBN信号进行6层小波分解，提取各层分解系数的均值和均方根，并讨论分析了各特征值随所加应力以及温度变化的相对变化关系。研究表明，在试样的弹性范围内，低频系数和各层高频系数的均值和均方根都随压应力的增加而减小;各层高频系数的均值和均方根随温度的升高而降低，低频系数的均值和均方根随温度的升高而升高。最后将温度、原始MBN信号以及各分解系数的均值和均方根作为神经网络的输入，压应力作为其输出建立神经网络模型，结果表明该神经网络模型与之前没有用小波分解时的神经网络模型相比，检测应力的准确性更高。

Because the wavelet transform has the characteristic of multi-scale analysis and it can characterize signals local feature <参考文献原文>, this article uses the wavelet decomposition method to study the sensitivity of different time-frequency components of the Magnetic Barkhausen Noise signal with changes in temperature and stress. After using the db5 wavelet with six layers to decompose the MBN signal, we extract the mean and RMS value of each layer decomposition coefficients and discuss the relationship between the relative change of the features and applied temperature and stress. It is found that within the elastic range of the sample, the mean and RMS value of high-frequency coefficients of each layer and low-frequency coefficients both decrease with increasing compressive stress. The mean and RMS value of high-frequency coefficients of each layer decrease, whereas the values of the low-frequency coefficients increase with increasing temperature respectively. This article takes temperature, the mean and RMS value of the original MBN signal and the decomposition coefficients as the input of the neural network and takes the stress as the output of the neural network to build the neural network model. It is shown that using this neural network model to detect stress has higher accuracy than the former BP neural network model in which the wavelet decomposition is not used.