In response to the challenges of low detection accuracy and poor stability in traditional wire rope damage detection techniques, we propose a novel approach based on the Support Vector Machine (SVM) wavelet kernel algorithm. This method leverages the similarity between kernel functions and wavelets in decomposition and synthesis processes, integrating kernel functions with wavelet denoising to effectively suppress interference noise. To achieve non-destructive inspection of wire ropes, we designed a magnetic leakage detection device. This device undergoes three analytical processing steps: trend removal of leakage magnetic signals, intervention-based signal denoising, and normalization of feature values for model training. This facilitates the detection, extraction, and identification of leakage magnetic signals. Through comparative analysis using simulated data, our results demonstrate superior detection performance of the proposed fault detection method.