改进灰狼算法优化支持向量机在风力机齿轮箱故障诊断中的应用

APPLICATION OF IMPROVED GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS

针对灰狼算法易陷入局部最优和后期寻优能力不足等缺点,提出改进非线性控制因子以提高算法收敛精度及稳定性。采用美国国家可再生能源实验室(National Renewable Energy Laboratory, NREL)"Gearbox Reliability Collaborative"项目测试采集的风力机齿轮箱振动信号为分析对象,经集合经验模态分解后,计算各本征模态函数分量的模糊熵并构建高维特征向量,后利用等距映射进行降维。利用改进灰狼算法优化支持向量机,对降维后齿轮箱故障特征集进行诊断。结果表明:改进灰狼优化算法相较于灰狼算法、粒子群算法和遗传算法可有效避免陷入局部最优并提高支持向量机诊断精度及稳定度,在不同测试样本下其准确率均最高,平均准确率达93.17%。

Aiming at the shortcomings of Grey Wolf Optimizer(GWO), such as it’s easy to fall into local optimum and insufficient mining capacity, the paper improve the convergence accuracy and stability of GWO based on the improvement of control factors. The wind turbine gearbox vibration signal collected by the "Gearbox Reliability Collaborative(GRC)" project of the National Renewable Energy Laboratory(NREL)in the United States was used as the analysis object. After the collection of Ensemble Empirical Mode Decomposition(EEMD), the fuzzy entropy of each eigenmode function component was calculated and the high dimensionality was constructed. Then feature vectors are used to reduce dimensionality using isometric mapping. The improved gray wolf algorithm is used to optimize the support vector machine to diagnose the gearbox fault feature set after dimensionality reduction. The results show that compared with GWO and PSO and GA, IGWO can effectively avoid falling into local optimum and improve the accuracy and stability of SVM diagnosis. It has the highest accuracy under different test samples, and the average accuracy rate can up to 93.17%.