Hybrid Conditional Kernel SVM for Wire Rope Defect Recognition

Support vector machine (SVM) has been applied in data classification and defect recognition in various scenes, the common kernels are not always suitable to satisfy the requirement of high accuracy and efficiency inspection. A new hybrid conditional kernel (HCK) and SVM model based on exponential functions is proposed in this article, the validity and feasibility of the basic characterizations are introduced theoretically first, and the numerical classification results for different datasets are calculated and compared with related algorithms. Then, the performance evaluation of the new model from the perspective of kernel parameter investigation, statistical data analysis, and experimental verification are conducted, and the wire rope defect inspection sample sets are built according to features extraction and eigenvalues computation. Finally, the comparison results where the new HCK based SVM can reach almost the highest classification accuracy of 91.70% and least running time of 0.1968 s for five different wire rope defects among 12 machine learning models not only prove the superior performance of the proposed method in wire rope defect recognition, but also show great promise in high precision classification in other application fields. The limitations of this model and future work are also discussed.