基于迁移学习的轴承剩余使用寿命预测方法

针对目前大多数基于人工智能的轴承剩余使用寿命(remaining useful life，RUL)预测方法不能很好地预测不同工况下轴承剩余寿命的问题，提出了一种基于迁移学习的寿命预测方法，对不同工况下的轴承进行剩余寿命预测.对采集的轴承原始振动信号进行傅里叶变换得到频域信号，以卷积神经网络和长短时记忆网络作为特征提取器对轴承频域信号进行特征提取并挖掘数据之间的时序信息，采用全局和局部域适应相结合的方法降低不同工况下轴承数据的分布差异.通过现有多种工况下轴承运行数据验证了该方法的有效性.与传统深度学习模型相比，所提方法提高了不同工况下轴承RUL预测精度.

Bearing Remaining Useful Life Prediction Method Based on Transfer Learning

To address the problem that most bearing remaining useful life (RUL) prediction methods based on artificial intelligence cannot well predict bearing RUL under different working conditions， a transfer learning method was proposed to predict bearing RUL under different working conditions. Fourier transform was applied to the raw vibration signals of the bearing to obtain the frequency-domain signals， and convolutional neural network (CNN) and long short-term memory network(LSTM) were used to extract the features between data of the bearing′s frequency-domain signals and mine temporal information. The method of combining global and local domain adaption was adopted to reduce the distribution differences of the bearing data under different working conditions. The effectiveness of the method was verified by the existing bearing data. Compared with the traditional deep learning models， the proposed method improves the accuracy of bearing RUL prediction under different working conditions.