基于MEEMD-SDP图像特征和DRN的行星齿轮箱故障诊断

Planetary gearbox fault diagnosis based on MEEMD-SDP image features and DRN

在行星齿轮箱齿轮的实际工程应用中,针对故障发生的早期阶段,其非平稳性、非线性振动特征信号导致故障诊断准确率低的问题,提出了一种基于MEEMD-SDP图像特征和深度残差网络的齿轮故障诊断方法。首先,采用了改进的集总平均经验模态分解(MEEMD)方法对齿轮振动信号进行了分解,获得了能够反映齿轮振动信号信息的固有模态函数(IMF);然后,通过对称点图案(SDP)分解方法提取了IMF分量,将其变换到极坐标下的雪花图像特征,并组成了特征向量;最后,引入深度残差网络(DRN)模型,实现了对行星齿轮箱齿轮不同故障的识别与分类,同时将其与卷积神经网络(CNN)模型进行了对比,并在东南大学公开的齿轮箱数据集上进行了不同模型对齿轮状态故障识别准确率的对比实验。研究结果表明:SDP图像特征能够全面表征齿轮的状态信息,相较于CNN模型,采用DRN模型对齿轮进行诊断得到的平均准确率有明显提高,可达到98.1%,能验证基于MEEMD-SDP图像特征和深度残差网络方法的有效性;研究结果对提升现有行星齿轮箱齿轮故障识别的准确率具有一定的价值。

In practical application,in order to solve the problem of low fault diagnosis accuracy due to the non-stationary nonlinear vibration signal of planetary gearbox in the early stage of fault development,a gear fault diagnosis method based on MEEMD-SDP image feature and deep residual network(DRN)was proposed.Firstly,modified ensemble empirical mode decomposition(MEEMD)was used to decompose gear vibration signals to obtain intrinsic modal function(IMF)components that could reflect gear vibration signals.Secondly,the IMF component extracted by symmetrized dot pattern(SDP)method was transformed into the feature vector of snowflake image features in polar coordinates.Finally,the deep residual network(DRN)model was introduced to realize the recognition and classification of different gear faults,and the comparison with the convolutional neural network(CNN)model was made.On the gearbox data set published by Southeast University,a comparative experiment was made on the identification accuracy of different models for gear state faults.The experiment results show that the SDP image features can fully represent the gear state information,and the average accuracy of DRN model for gear diagnosis is significantly higher than that of CNN model,reaching 98.1%.The research results have certain theoretical and practical value for improving the accuracy of gear fault identification of existing planetary gearboxes.