基于EEMD和ELM的齿轮故障状态识别

Gear Fault Diagnosis Method Based on the Extreme Learning Machine

由于传统智能故障诊断方法所需调整参数多且难以确定、训练速度慢,致使齿轮轴承故障分类精度、效率差的问题,提出一种基于集合经验模态分解与极限学习机结合的齿轮诊断方法。首先将采集的信号经EEMD后,提取与原信号相关较大的IMF能量指标,建立齿轮的极限学习机故障分类模型;最后,将能量指标组成的特征向量作为模型输入进行齿轮不同故障状态的分类识别。把ELM识别的结果与SVM识别结果作对比,结果表明ELM的齿轮故障诊断方法具有较快的运行速度、较高的分类精度。

Because of the traditional intelligent fault diagnosis method is needed to adjust many paramaters that is difficult to determine and has slow training speed, the rolling bearing fault classification accuracy and efficiency is not satisfied. In this paper,a gear fault diagnosis method based on extreme learning machine is put forward. First of all, it extracts the energy of the IMF that has larger correlation with the original signal.Then,f ault classification model based on extreme learning machine of gear is established. Finally,the feature vector of energy index is inputed to the model to identify the different failure states. The experimental results show that compared with the SVM gear fault diagnosis method based on extreme learning machine has faster speed and higher classification accuracy.