The invention relates to a steel rail crack detection method based on multiple
acoustic emission event probabilities. According to the steel rail crack detection method, the
relative probability output by a
convolutional neural network is used as the probability of an
acoustic emission event, and the problem that
temporal information between samples is not fully used by an existing steel rail crack detection method is solved. The steel rail crack detection method comprises the steps of (1) loading an
acoustic emission time domain signal data matrix, and performing FFT (Fast Fourier Transformation) and pretreatment on acoustic emission signals, so that a spectral matrix which is folded into a three-dimensional matrix and a
label vector are obtained; (2) setting structural parameters and an initial value of the convolutional network; (3) inputting the spectral matrix, calculating and iterating errors of a
convolutional neural network model layer by layer, updating a weight matrix and bias, performing
feature extraction, and outputting classification results and classification probabilities of a
test set; (4) correcting the outputting of the
convolutional neural network on the basis of the multiple acoustic emission event probabilities, and optimizing the classification results. According to the steel rail crack detection method, the classification results are improved according to the multiple acoustic emission event probabilities, so that the detection precision of steel rail crack damages is increased, and high theoretical and practical
engineering significance is obtained.