Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM

The challenge in reusing high-impact recorders lies in developing an efficient and accurate failure prediction model under small-sample conditions. To address this issue, this study proposes an IPSO-SVM model. First, the particle swarms in the IPSO algorithm were grouped based on their exploration and exploitation functions, and dynamic inertia weight mechanisms were designed accordingly. The grouping ratio was dynamically adjusted during iterations to enhance optimization performance. Tests using benchmark functions verified that this approach improves convergence accuracy and stability compared to conventional PSO algorithms. Subsequently, the 5-fold cross-validation accuracy of the SVM model was used as the fitness value, and the IPSO algorithm was employed to optimize the penalty and kernel parameters of the SVM model. Trained on high-impact experimental data, the IPSO-SVM model achieved a prediction accuracy of 90.5%, outperforming the PSO-SVM model’s 85%. These results demonstrate the potential of the IPSO-SVM model in addressing failure prediction challenges under small-sample conditions.