基于 CEEMDAN 与改进核极限学习机的S700K 转辙机健康状态诊断

针对S700K转辙机健康状态分类过于粗放、诊断速度慢、效率低的问题，提出一种基于CEEMDAN与改进核极限学习机(kernel based extreme learning machine, KELM)的诊断方法。首先，对S700K转辙机功率数据进行自适应噪声完备集合经验模态分解，得到6个本征模态函数(intrinsic mode function, IMF);然后，计算本征模态函数的模糊熵值(fuzzy entropy, fuzzyEn, FE)作为表征转辙机健康状态的特征参数；最后，利用麻雀算法(sparrow search algorithm, SSA)改进的核极限学习机对9种健康状态进行健康诊断，并与SVR和ELM模型进行对比。仿真结果表明，改进核极限学机模型准确率、精确率、召回率等指标分别达到97.8%、98.0%、97.8%,相较于SVR和ELM模型，SSA-KELM模型在保证运行速度的基础上，将诊断准确率至少提高2.2%。

Health state diagnosis of S700K switch machine based on CEEMDAN and improved kernel based extreme learning machine

Aiming at the problems of extensive classification of health status of S700K switch machine, slow diagnosis speed and low efficiency; a diagnosis method based on CEEMDAN and kernel based extreme learning machine (KELM) is proposed. Firstly, the power data of S700K switch machine is decomposed by adaptive noise complete set empirical mode decomposition, and six intrinsic mode functions (IMF) are obtained. Then, the fuzzy entropy (FE) value of the intrinsic mode function is calculated as the characteristic parameter to characterize the health state of the switch machine. Finally, the kernel limit learning machine improved by sparrow search algorithm (SSA) is used to diagnose nine health states, and compared with SVR and ELM models. The simulation results show that the accuracy rate and the recall rate of the improved kernel based extreme learning machine model are 97. 8%, 98. 0% and 97. 8% respectively. Compared with SVR and ELM models, SSA-KELM model improves the diagnostic accuracy rate by at least 2. 2% on the basis of ensuring the running speed.