# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 1 Introduction  
High-impact recorders are crucial equipment in the development of new weapon systems. They capture critical data during weapon flight, such as acceleration, angular velocity, and pressure, enabling weapon developers to verify the operational status of the equipment and perform fault analysis [1, 2]. In the past, after enduring a single high-impact test, it was difficult to determine whether the recorder could be reliably reused, so it was typically discarded. To reduce weapon development costs, there has been increasing interest in developing reusable high-impact recorders, making fault prediction a pressing challenge. Currently, fault models for instruments and equipment are generally divided into physical models and data-driven models. Data-driven models have significant advantages over traditional physical models, particularly for complex and nonlinear systems, as they can reveal fault patterns and system behaviors through learning and analyzing historical operational data. Common data-driven models include neural networks, support vector machines (SVMs), and other methods [3, 4]. In recent years, neural networks have been widely applied in fault prediction and health management. For instance, one study [5] proposed using spiking neural networks for fault prediction in syntactical time series. Another study [6] introduced a method combining particle swarm optimization (PSO) and backpropagation neural networks (BPNN-PSO) for fault diagnosis in photovoltaic arraysystems. These studies illustrate that neural networks show good performance and wide applicability in fault prediction. However, training most neural networks requires large amounts of high-quality data, which is often limited during the development of many instruments. Under small-sample conditions, the performance of neural networks may not be as effective as desired. In such cases, SVM models may be a better option. One study [7] demonstrated the application of SVM for fault diagnosis in rotating machinery, highlighting its strong performance under small-sample conditions. Additionally, another study [8] noted that, with appropriate preprocessing methods, SVM could achieve excellent classification results in certain cases. Research [9] tested various methods, including PSO-SVM, HHO-SVM, and MFO-SVM, and confirmed their effectiveness in optimizing SVM models, achieving promising results across different aspects. These findings suggest that SVM models perform well for small-sample fault prediction. However, their performance is influenced by the selection of penalty and kernel parameters. In traditional SVM models, these parameters are often determined based on empirical values, leading to unstable performance. To improve fault prediction accuracy, it is necessary to explore suitable optimization algorithms for determining SVM parameters. The particle swarm optimization (PSO) algorithm is a commonly used function optimizer that can improve SVM fault recognition accuracy to some extent. However, it is prone to premature convergence and getting trapped in local optima, indicating room for enhancement [10]. In recent years, many advanced optimization algorithms have been proposed and applied to solve complex problems. For instance, [11] reviews the development of the Whale Optimization Algorithm (WOA), analyzing 59 improved variants and 57 hybrid versions, and suggests effective strategies to enhance exploration capabilities and optimization performance. Reference [12] introduces a novel exploration strategy for the YUKI algorithm, which dynamically adjusts exploration and exploitation strategies and integrates gradient descent to improve performance in topology optimization, significantly enhancing its exploration ability. These studies provide valuable insights and references for improving SVM parameter optimization. In this paper, we propose an improved PSO-SVM model (hereinafter referred to as IPSO-SVM) to address the challenge of fault prediction for high-impact recorders under small-sample conditions.