Wire rope damage identification method based on 1D-CNN-SVM

To achieve efficient and accurate identification of wire rope damage, a wire rope damage identification model based on 1D-CNN-SVM was proposed. A one-dimensional convolutional neural network was used to extract features from the magnetic flux leakage detection signal of damage, and then extracted features were input into a support vector machine for defect classification. By substituting datasets from different operating speeds into the model, the defect recognition ability of the proposed model was tested. The experimental results showed that compared to models such as 1D-CNN, 1D-CNN-ELM, and 1D-CNN-LTSM, the proposed model had higher accuracy and reliability, with an accuracy rate of no less than 97% for identifying various types of damage, demonstrating strong generalization ability.