重要性特征筛选协同随机森林的舵机次品筛查

Servo Defect Screening Based on Importance Feature Selection and Random Forest

摘要:

舵机作为航行控制系统的核心执行机构,其运行状态直接决定了运载工具的动态品质与航行安全。针对传统检测方法效率低、精度差,且舵机测试数据具有高维冗余性导致分类性能不足的问题,提出一种基于重要性特征筛选的随机森林(IFS-RF)舵机次品筛查方法。该方法首先通过随机森林算法定量评估原始特征的重要性并进行降序排列,构建最优关键特征子集,以此消除冗余维度对分类决策的干扰；在此基础上,利用网格搜索与交叉验证算法对随机森林分类器进行参数寻优,建立高泛化的二分类筛查模型。测试结果表明,IFS-RF算法能够精准识别测试数据中的异常样本,显著提升了分类精度与模型稳定性,为智能化舵机质量检测提供了有效的技术支撑。

Abstract:

As a core execution component of navigation control systems, the operational status of the servo mechanism directly dictates the dynamic quality and navigational safety of vehicles. To address the low efficiency and insufficient precision of traditional screening methods, as well as the degraded classification performance caused by high-dimensional redundancy in test data, this paper proposes a servo defect screening method based on Importance Feature Selection and Random Forest (IFS-RF). The proposed approach quantitatively evaluates and ranks the importance of original features via a random forest algorithm to construct an optimal critical subset, thereby eliminating the interference of redundant dimensions on classi-fication decisions. On this basis, grid search and cross-validation algorithms are integrated to optimize the parameters of the random forest classifier, establishing a highly generalizable binary classification screening model. Validation results demonstrate that the IFS-RF algorithm accurately identifies anomalous samples within the test data, significantly improving classification accuracy and model stability. This work provides an effective and reliable technical framework for intelligent servo quality inspection.