基于IEWT-DELM的行星齿轮箱故障诊断

针对在恶劣情况下行星齿轮箱特征难以提取以及多种故障状态下难以准确分类这种问题，提出在经验小波变换基础上将原有频谱分解替换为在噪声干扰下更为稳定的尺度谱分解的改进经验小波变换与深度极限学习机相结合的故障诊断方法。首先，将行星齿轮箱不同故障工况下的信号利用改进经验小波变换分别进行降噪处理并提取各阶调频-调幅分量，之后选取包络幅值峭度较高的前6个分量多尺度样本熵作为故障特征集，输入到深度极限学习机中进行故障诊断分类，行星齿轮箱故障诊断试验表明：与EWT、EMD与DELM结合的故障诊断准确率相比，该方法故障平均识别率可达97.6%,具有一定的有效性。

Planetary gearbox fault diagnosis based on IEWT-DELM

Aiming at the problem that it is difficult to extract the features of planetary gearboxes under harsh conditions and difficult to classify accurately under various fault states. Based on the Empirical Wavelet Transform, the Improved Empirical Wavelet Transform is proposed, which replaces the original spectrum decomposition with the scale-spectrum decomposition which is more stable under noise interference. A fault diagnosis method combining Improved Empirical Wavelet Transform and Deep Extreme learning machine. Firstly, the signals of the planetary gearbox under different fault conditions are denoised by IEWT respectively and the FM-AM components of each order are extracted. Then, Multiscale sample entropy of the first six components with higher Envelope spectrum kurtosis was selected as the fault feature set and input into DELM for fault diagnosis and classification. The results of planetary gearbox fault diagnosis test show that compared with the fault diagnosis accuracy of EWT, EMD and DELM, the average fault recognition rate of this method can reach 97.6%, which has certain effectiveness.