# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 4 Discussion  
This study introduced the IPSO-SVM model to address the challenge of fault prediction for high-impact recorders under small sample conditions. By improving the Particle Swarm Optimization (PSO) algorithm through particle grouping, dynamic inertia weight mechanisms, and probabilistic acceptance of inferior solutions, the IPSO algorithm made significant progress in addressing the issues of premature convergence and local optima inherent in traditional PSO algorithms. Experimental and simulation data demonstrated that \begin{table}
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**Parameters** & **Value** \\ \hline Number of hidden units & 128 \\ Activation function & ReLU \\ Optimization algorithm & Adam \\ Learning rate & 0.001 \\ Batch size & 32 \\ Training epochs & 50 \\ \hline \hline \end{tabular}
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Table 10: The basic parameters of the LSTM model. \begin{table}
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**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline IPSO-SVM & 90.5\% & 95.5\% & 85.0\% & 89.9\% \\ LSTM & 89.0\% & 91.5\% & 86.0\% & 88.7\% \\ Random Forest & 83.5\% & 86.0\% & 80.0\% & 82.9\% \\ \hline \hline \end{tabular}
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Table 12: Prediction Results of the IPSO-SVM model and other models. the prediction accuracy of the IPSO-SVM model reached 90.5%, significantly outperforming the traditional PSO-SVM model. The implications of this study are substantial. Firstly, it offers an effective technical solution to enhance the reusability of high-impact recorders, reducing potential costs associated with repeated use of the equipment. The IPSO-SVM model can also play a critical role in fault prediction tasks where the data are limited, high-dimensional, and nonlinear, making it particularly suitable for industrial applications where data are scarce or expensive to obtain. In addition to the high-impact recorder applications, the IPSO-SVM model shows considerable promise in a variety of other industrial contexts. For instance, devices like acceleration recorders and free-field shock wave pressure recorders typically operate in extreme environments, with unpredictable reliability after each use. By collecting a small sample of historical operational data, the IPSO-SVM model can predict the future operational conditions of these devices, thus reducing the costs associated with frequent device replacements or failures caused by multiple uses. This is particularly valuable in industries, such as aerospace, defense, and energy, where equipment downtime or failure can be costly and detrimental. Moreover, the IPSO-SVM model's effectiveness extends beyond high-impact equipment. It can also be applied in medical device development, where certain high-cost equipment cannot be used to gather large datasets. In scenarios involving small sample sizes, such as with costly medical instruments, the IPSO-SVM model can provide reliable fault predictions, helping to save on development costs and ensuring the accuracy of the devices without needing extensive testing datasets. This demonstrates the model's flexibility and potential for a wide range of applications, from high-end manufacturing to healthcare technology. Despite these advancements, the study does acknowledge several limitations that offer opportunities for future research. Limitations of the Model Research:  
1. Limited dataset size: Future research could explore the robustness of the model by incorporating more diverse samples or different types of failure data. 2. Choice of optimization algorithm: Although the IPSO algorithm performed well in this study, other optimization algorithms (e.g., genetic algorithms or ant colony optimization) may perform better in different scenarios. Further comparative studies should be considered. Analysis of Fault Prediction for High-Impact Recorders:  
1. Data analysis results: Analysis of the data reveals that the model's success rate in predicting label 0 (undamaged) is higher than that for predicting label 1 (damaged).