基于IEWT和IWAAE的滚动轴承故障识别研究

Rolling bearing faults identification based on IEWT and IWAAE

通过深度学习实现轴承故障识别时,存在着因信号噪声导致故障识别率较低的问题,针对这一问题,提出了一种基于改进经验小波变换(IEWT)和改进Wasserstein自编码器(IWAAE)的轴承故障识别方法。首先,将轴承振动数据由时域变换到包络谱域,通过包络谱的极值点与自适应阈值的关系对其进行了包络谱自动分割,进而利用经验小波变换,将数据自动分解为不同频段的调幅调频分量,并采用改进峭度指标对选取合适的分量进行了重构,进而对信号进行了有效降噪;然后,针对变分自编码器训练困难的缺陷,引入Wasserstein自编码器,根据Wasserstein自编码器中间层神经元的激活值大小,对神经元进行了自动增加或删减,进而构造了IWAAE;最后,将重构信号输入到IWAAE中,进行了滚动轴承故障特征的自动提取和故障识别。研究结果表明:与其它的轴承故障识别方法相比,采用IEWT-IWAAE方法的故障识别精度更高,准确率可达99.28%,标准差仅0.32;该方法能在一定程度上缓解传统方法对人工特征提取和特征选择的依赖,其对噪声的鲁棒性高,故障识别能力优于其他组合模型方法。

When realizing bearing fault identification through deep learning,there was a low recognition rate due to signal noise.Aiming at this problem,a combined model based on improved empirical wavelet transform(IEWT)and improved Wasserstein auto-encoder(IWAAE)was proposed.Firstly,the collected bearing vibration signals were transformed by envelope spectrum,and envelope spectrum adaptive segmentation was implemented through the relationship between the envelope point and the adaptive threshold,so that the bearing vibration signals were adaptive decomposed into AM-FM components,and the appropriate components were selected by the improved kurtosis index and reconstructed to effectively reduce the noise of signals.Secondly,for the difficulties in training the variational auto-encoder,the Wasserstein autoencoder was introduced and the IWAAE was adaptively increased or deleted for the neurons according to the strength of their activation in its hidden layer.Finally,the noise-reduction signals were fed into IWAAE for automatic feature extraction and fault identification.The results of the study indicate that the classification accuracy of IEWT-IWAAE is higher,comparing with other bearing fault identification methods,the accuracy can reach 99.28%,with a standard deviation of only 0.32.The method alleviates the dependence of traditional methods on tedious artificial feature extraction and artificial feature selection to some extent and has high robustness to noise and the fault recognition ability is better than other combined model methods.