Harbin University of Science and Technology
非平稳工况下的齿轮故障检测是一项非常困难的工作，由于齿轮振动信号的复杂性，导致故障特征提取和故障诊断困难.针对这些问题，本文基于径向基（Radial Basis Function，RBF）神经网络，提出了一种在变速条件下齿轮的故障诊断的方法CIHDRFD.在CIHDRFD方法中，首先利用自适应白噪声的完整集成经验模态分解（Complete Ensemble Empirical Mode Decomposition with Adaptive Noise，CEEMDAN）将原始振动信号分解为多个固有的模态函数（Intrinsic Mode Function，IMF），并通过计算其信息熵（Information Entropy，IE），筛选出IE最小的4个IMF作为特征IMF.然后，利用希尔伯特变换（Hilbert Transform,HT）处理特征IMF并求出Hilbert包络谱，利用Hilbert包络谱构建故障特征向量.最后，利用改进的双RBF神经网络进行故障检测.本文通过搭建齿轮故障检测平台验证了CIHDRFD方法的有效性.实验结果表明，CIHDRFD方法适用于齿轮故障诊断，在速度波动为3%的情况下，CIHDRFD方法诊断准确率和诊断时间分别为98.21%和74.53s.
It is a very difficult work to detect gear fault under non-stationary condition, due to the complexity of gear vibration signals, it is difficult to extract fault features and diagnose faults. In order to solve these problems, based on radial basis function (RBF) neural network, this paper proposes a gear fault diagnosis method CIHDRFD. In the CIHDRFD method, first use complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original vibration signal into multiple inherent modal functions (IMF), and by calculating its information entropy (IE), the 4 IMF with the smallest IE are selected as the characteristic IMF. Then, the Hilbert transform (HT) is used to process the feature IMF and the Hilbert envelope spectrum is obtained. The Hilbert envelope spectrum is used to construct the fault feature vector. Finally, the improved double RBF neural network is used for fault diagnosis. In this paper, the effectiveness of the CIHDRFD method is verified by building a gear failure detection platform. Experimental results show that the CIHDRFD method is suitable for gear fault diagnosis. When the speed fluctuation is 3%, the diagnostic accuracy and diagnosis time of the CIHDRFD method are 98.21% and 74.53s, respectively.