Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability ( IF 2.1 ) Pub Date : 2020-10-23 , DOI: 10.1177/1748006x20964614 Fafa Chen 1, 2, 3 , Lili Liu 1, 3 , Baoping Tang 2 , Baojia Chen 1 , Wenrong Xiao 1 , Fajun Zhang 1
The fault features of gearbox are often influenced and interwoven with each other under the non-stationary condition. The traditional shallow intelligent diagnosis models are difficult to detect and identify gearbox faults with selected features according to prior knowledge. To solve this problem, a novel deep convolutional auto-encoding neural network is designed based on the fusion of the convolutional neural network with the automatic encoder in this research. The vibration signals of gearbox are transformed into Hilbert envelope spectrum by using Hilbert transform and Fourier transform, and the different characteristics of spectral spatial data are automatically learned by convolutional auto-encoding neural network with multiple convolution kernels. The parameters of the convolutional neural network are fine-tuned through a fully connected neural network with a small number of labeled samples. Through the analysis for gearbox fault experiments, the effectiveness and practicability of the proposed method in equipment fault diagnosis are verified. The deep convolutional neural network embedded in the auto-encoder has stronger learning ability, and the diagnosis performance is more stable and reliable in practical engineering application.
中文翻译:
自动编码器深度卷积神经网络融合的新方法及其在行星齿轮箱故障诊断中的应用
在非平稳状态下,变速箱的故障特征经常会相互影响并交织在一起。传统的浅层智能诊断模型难以根据先验知识检测和识别具有选定特征的变速箱故障。为解决这一问题,本研究在卷积神经网络与自动编码器融合的基础上,设计了一种新型的深度卷积自动编码神经网络。利用希尔伯特变换和傅里叶变换将齿轮箱的振动信号变换为希尔伯特包络谱,并通过具有多个卷积核的卷积自动编码神经网络自动学习光谱空间数据的不同特征。卷积神经网络的参数通过带有少量标记样本的完全连接的神经网络进行微调。通过对齿轮箱故障实验的分析,验证了该方法在设备故障诊断中的有效性和实用性。嵌入在自动编码器中的深度卷积神经网络具有较强的学习能力,在实际工程应用中诊断性能更加稳定可靠。