考虑滚动轴承故障位置与损伤程度的双分支卷积神经网络故障诊断方法

针对现有深度学习方法对非平稳的滚动轴承故障诊断过程中，先验故障信息利用不充分和故障样本不完备，导致诊断精度不高甚至无法诊断的问题，充分发掘轴承故障位置和损伤程度与振动数据特征间的映射关系，提出一种考虑滚动轴承故障位置与损伤程度的双分支卷积神经网络故障诊断方法。该方法首先将原始振动信号矩阵化，构建二维灰度图像数据集，然后建立双分支的改进VGGNet深度卷积网络，将故障位置与损伤程度进行双标签二值化，每个分支独立提取深层特征，实现故障位置和损伤程度特征与标签的自适应。仿真实验结果表明，相较其他深度学习方法，所提方法能够在部分先验知识缺失条件下，实现滚动轴承潜在的不同故障位置及损伤程度的多状态分类，获得较高准确率的同时兼具良好的抗噪性能。

Double-branch Convolutional Neural Network Fault Diagnosis Method Considering the Fault Location and Damage Degree of Rolling Bearings

Aiming at the problems of insufficient use of prior fault information and incomplete fault samples, the existing deep learning methods in the fault diagnosis process of non-stationary rolling bearings have low accuracy or even failure to diagnose. This paper proposed a double branch convolutional neural network method for bearing fault diagnosis based on the research of fault location and damage degree. Firstly, a point transfer matrix was used to process the original vibration signals of rolling bearings, and a two-dimensional image data set was constructed. Secondly, a double branch improved VGGNet deep convolutional network was constructed. One-hot coding of fault location and damage degree separately, so the features and labels of fault location and damage degree can be adaptive simultaneously. The simulation and experimental results show that compared with other neural networks, the proposed method can achieve the multi-state classification of the potential different fault locations and damage degrees of rolling bearings and still maintains good diagnostic performance under the condition of partial lack of prior knowledge.