基于CEEMDAN与VNWOA-LSSVM的供输弹系统早期故障诊断研究

由于供输弹系统早期故障信号成分复杂，故障特征微弱，故提出一种基于自适应噪声完备经验模态分解(CEEMDAN)与以冯诺依曼拓扑结构(VN)改进鲸鱼算法(WOA)优化下的最小二乘支持向量机（LSSVM）的故障诊断方法。在对所测信号进行预处理即去趋势项和零点漂移后，通过CEEMDAN对供输弹信号进行分解，得出模态分量(IMF); 然后依据相关系数和峭度准则这两个标准来选取符合标准的IMF分量，提取这些分量的分布熵(DE)作为特征; 最后用VNWOA-LSSVM诊断模型，输入供输弹系统3种不同工况下的振动信号特征进行故障诊断，并且还对比了LSSVM、PSO-LSSVM、GA-LSSVM和WOA-LSSVM等方法对故障的识别率。实验结果表明：这些方法中经VNWOA优化后的LSSVM的识别率最高，高达94.03%。

Research on Early Fault Diagnosis of Ammunition Supply and Transportation System Based on CEEMDAN and VNWOA-LSSVM

Due to the complexity of signal components and weak fault features in the early fault of ammunition feeding and conveying system, a fault diagnosis method based on adaptive noise complete empirical mode decomposition (CEEMDAN) and least squares support vector machine (LSSVM) optimized by von Neumann topology (VN) improved whale algorithm (WOA) was proposed. After preprocessing the measured signal, i.e. removing the trend term and zero drift, the feed signal was decomposed by CEEMDAN to obtain the modal component (IMF).Then, according to the correlation coefficient and kurtosis criterion, the IMF components that met the criteria were selected, and the distribution entropy(DE) of these components was extracted as the feature. Finally, the fault diagnosis model of VNWOA-LSSVM was used to diagnose the vibration signal of the ammunition feeding system under three different working conditions, and the fault recognition rates of LSSVM, PSO-LSSVM, GA-LSSVM and WOA-LSSVM were compared. The experimental results show that the recognition rate of VNWOA-LSSVM is the highest, and it is up to 94.03%.