在对钢轨温度应力检测的过程中，由于温度和应力对MBN信号都存在影响，因此如何对数据进行温度补偿得到准确的应力值是研究的重点。基于BP人工神经网络，以铁磁性试件的温度、和经处理得到的巴克豪森噪声信号的均值、均方根、振铃数和峰宽比作为主要的影响因子，以试件的压应力作为输出结果，建立巴克豪森铁轨温度应力检测系统。使用多个样本对该网络进行训练后，采用若干测试样本对网络进行测试，最后将测试结果与实际设计结果相对比，得出网络的平均准确率达到了测试的目标，说明了该基于BP神经网络的巴克豪森温度应力检测系统能够实现对温度的补偿，具有高效性和准确性。

Due to the fact that both of the temperature and stress have effect on MBN signals, in the detection process of the rail temperature stress, so how to get the accurate stress value by temperature compensating for the test stress was a key point of this study. A temperature stress detection system by Barkhausen noise, based on the BP neural network, was built by taking the temperature of the ferromagnetic specimens, the frequency and voltage of the excitation signal, and the mean value, RMS value, ringing counts and peak-wide ratio of the processed Barkhausen signal as the main impact factor, and taking the corresponding compressive stress of specimen as the output data. After being trained by many training samples, the network was tested by some test samples. It showed that the required accuracy was reached by comparing the test results with the actual stress value, which indicated that the stress detection system by Barkhausen noise based on BP neural network obtained the perfect temperature compensation, and was very efficient and accurate.