Abstract: To solve the problem that the influence of tuning zone fault on train safety is neglected in existing fault diagnosis, a voltage model of locomotive signal in the shunting state is established to obtain the signal voltage of locomotive in normal state and different fault state of tuning zone. Then, the initial database is expanded to obtain the data set of tuning zone fault by considering the influence of compensation capacitance fault and different ballast resistance on the signal voltage of locomotive. On that basis, Convolutional Neural Network is used to realize fault diagnosis of tuning zone. Hereinto, feature extraction is realized by convolution layer in the CNN and the relatively optimal convolution layer parameters are selected by comparing different convolution layer parameters in term of the accuracy of diagnostic and training time under current condition. Furthermore, dropout function is used to avoid overfitting in training, and fault classification is realized through the second full connection layer in the CNN. To deal with human errors in data labeling when constructing the data sets artificially, wrong labeled data sets is constructed to reduce the influence of wrong labeled data on training process. The test results show that the accuracy of the test set is 97.92% when the SNR is 40 dB and this diagnosis method is still effective in a noisy environment.