Research on Defect Recognition of Steel Wire Ropes Based on Multi-Channel One-Dimensional Convolutional Neural Network Exploring Convolutional Neural Networks and Their Applications

In response to the limitations of single-channel steel wire rope defect detection based on flux leakage testing and current recognition algorithms in practical engineering, a research study on defect recognition of steel wire ropes based on a multi-channel one-dimensional convolutional neural network (CNN) is proposed. This method extracts data features implicitly through a multi-point measurement of the magnetic flux leakage field of the steel wire rope, fuses multi-channel data, and thus achieves effective defect detection. The results show a significant improvement in recognition accuracy, with a prediction accuracy of 0.985. Additionally, the introduction of an early stopping mechanism greatly enhances the model's training efficiency, and comparative analysis demonstrates that the multi-channel architecture converges faster and achieves better recognition accuracy.