Experimental study on train axle fatigue crack acoustic emission signals recognition based on a one-dimensional convolutional neural network

The method for identifying train axle fatigue cracks based on acoustic emission technology requires the classification and processing of acoustic emission signals. However, existing methods for recognizing axle fatigue crack acoustic emission signals, including parameter analysis, wavelet analysis, and traditional machine learning algorithms, usually rely on expert experience, resulting in low detection efficiency. Additionally, the traditional machine learning algorithms above typically have shallow structures, which limits their ability to extract deeper feature information. Therefore, this paper proposes a method that uses a one-dimensional convolutional neural network (1D-CNN) to identify train axle fatigue crack acoustic emission signals. This method achieves end-to-end intelligent recognition of acoustic emission signals, eliminating the need for manual intervention or pre-set feature extractors. Furthermore, it leverages deep learning mechanisms to deeply mine the complex features within the signals, thereby enhancing the accuracy of the model's identification. Experimental validation was conducted using the acoustic emission signals collected from the axle fatigue crack test rig. The results demonstrate that this model can accurately identify acoustic emission signals of axle fatigue cracks, achieving an identification accuracy of over 99%. Meanwhile, compared with other network models, the effectiveness of the proposed model is further verified.