Crack classification and quantitative evaluation based on dimensionality reduction optimization model of multifeature weak magnetic signal

In this paper, we propose a classification and quantitative method based on the optimization model of dimensionality reduction for detecting crack defects in superalloy turbine disks using multifeature weak magnetic signals. First, the experiment on magnetic weak nondestructive testing was conducted on the defects, resulting in the acquisition of 112 sets of preset magnetic anomaly eigenvalue data. The dimensionality of the multidimensional weak magnetic signal features was then reduced using the principal component analysis method to construct the weak magnetic feature sample library. On this basis, a support vector machine (SVM) model was established to classify and quantitatively evaluate the defects. Genetic algorithm and grid search method were used to optimize the model. Finally, the validity of the optimized SVM model was verified by adding artificial and natural crack defect specimens outside of the sample library. Results show that compared with the original model, the feature dimension reduction and optimization of the two methods enhance the classification accuracy of superalloy surface defects and the quantitative accuracy of defect length, width, and depth. The genetic algorithm significantly improved the classification accuracy, increasing it by 39.33 %. Specifically, the genetic algorithm improved the quantitative accuracy of length and width by 17.9 % and 28.56 % respectively. On the other hand, the grid search method improved the quantitative accuracy of depth the most, with an improvement of 49.83 %. For specimens with natural cracks, the optimized SVM model still demonstrated good classification and quantitative effectiveness in detecting defects, with an accuracy rate of over 85 %.