基于GWO-VMD算法的齿轮故障自适应特征提取

Adaptive Feature Extraction of Gear Fault Based on GWO-VMD Algorithm

目的 齿轮产生故障时,利用其声发射信号进行自适应特征提取后诊断。方法 利用变分模态分解方法(VMD)对齿轮发生故障时的声发射信号进行分解。在现实状况中,采集声发射原信号噪声干扰大,导致特征提取准确度低,并且模态分解时参数需要人为调试设定。鉴于此,引入灰狼优化算法(GWO),对模态分解个数k和二次惩罚因子α自适应选择最优参数后,对信号分解得到本征模态函数(IMF)。通过相关系数选出最佳IMF作为特征分量,计算其峭度和样本熵。结果 计算了各分量的相关系数,选取与原始信号最为相近的分量,分别计算其峭度和样本熵。分解后,齿轮故障声发射信号峭度高于正常的情况,而样本熵则偶然性表现为正常情况下的值大于故障条件下的值。结论 采用支持向量机对特征向量集进行分类识别,对比改进后的试验结果,GWO-VMD结合峭度–样本熵的方法能够有效地提取故障特征,判断齿轮状态是否健康。

The work aims to make use of the acoustic emission signal when fault occurs for adaptive feature extraction and diagnosis. The variational mode decomposition method(VMD) was used to decompose the acoustic transmission signal when fault occurs. In reality, collecting acoustic signal noise interference causes low feature extraction accuracy due to the large original noise interference, and mode decomposition parameters need artificial debugging set. The Gray Wolf Optimization(GWO) algorithm was introduced to obtain the intrinsic mode function(IMF) through signal decomposition after adaptive selection of optimal parameters for mode decomposition k and quadratic penalty factor α. The peakedness and sample entropy were calculated by selecting the based on correlation coefficient. The correlation coefficient of each component was calculated. The peakedness and the sample entropy of the component most similar to the original signal were calculated respectively. After the decomposition, the gear fault acoustic emission signal was higher than normal, while the sample entropy was more than the normal value under the fault condition. Support vector machine is used to classify and identify feature vector sets. Compared with the improved experimental results, GWO-VMD combined with peakedness-sample entropy method can effectively extract fault features and determine whether the gear state is healthy.