Research on Ultrasonic Testing Data Classification based on Particle Swarm Optimization Support Vector Machine

Abstract

Addressing the challenges of complex signal characteristics and difficult fault type differentiation in ultrasonic detection, a fault classification method based on Particle Swarm Optimization Support Vector Machine (PSO-SVM) is proposed. This method utilizes the particle swarm optimization algorithm to globally optimize key parameters in the support vector machine model, overcoming the limitations of traditional support vector machines, such as relying on experience for parameter selection and unstable classification performance. Taking experimental data collected from ultrasonic detection as the research object, the original signals are first subjected to feature extraction and preprocessing. Subsequently, PSO-SVM and traditional SVM models are constructed for fault pattern recognition and classification experiments, respectively. By comparing and analyzing the performance of the two methods in terms of classification accuracy and other performance indicators, the experimental results demonstrate that the PSO-optimized SVM model outperforms the standalone SVM method in both fault classification accuracy and overall recognition effectiveness. This verifies the effectiveness and feasibility of the proposed method in ultrasonic detection fault classification.