The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness.

可变工况下获得的滚动轴承振动信号不服从相同的独立分布，因此传统的轴承故障诊断方法精度较低，这是一种基于稀疏去噪自动编码器（SDAE）的滚动轴承故障诊断方法，用于深度特征提取。提出了结合转移学习的方法。首先，通过傅立叶变换将时域中的轴承振动信号转换为频域信号，然后将其输入到SDAE中以进行自适应深度特征提取。然后，引入联合的几何和统计对齐方式来处理深度特征样本，以减少统计和几何上的域差异。最后，采用k近邻分类算法完成滚动轴承在不同工况下的故障诊断。实验结果表明，本文提出的方法提高了可变工况下滚动轴承故障诊断的准确率，验证了其可行性和有效性。