正交约束域适应的跨工况滚动轴承剩余使用寿命预测方法

A Domain Adaptive Method with Orthogonal Constraint for Predicting the Remaining Useful Life of Rolling Bearings under Cross Working Conditions

针对跨工况轴承剩余使用寿命(RUL)预测模型的决策边界不明显、特征可辨识性低的问题,该文提出一种正交约束的最大分类器差异方法(MCD_OC)。首先,将采集的轴承原始振动信号进行快速傅里叶变换,得到振动信号的频域信号作为模型的输入;然后,通过卷积神经网络(CNN)和门控循环神经网络(GRU)提取轴承信号的深层时空特征,利用最大分类器差异将源域和目标域特征对齐,并对目标域轴承深层特征进行正交约束,增大无标签目标域样本特征之间的可辨识性;最后,基于轴承寿命数据集开展了跨工况轴承寿命预测对比实验,对该文所提方法进行评估,并在多组实验中取得最优结果。

To address the problems that blurred decision boundaries and low identifiability of features in the rolling bearing Remaining Useful Life(RUL)prediction under cross working conditions,a domain adaptive method with Maximum Classifier Discrepancy network with Orthogonal Constraints(MCD_OC)is proposed.Firstly,the fast Fourier transform is applied to transform the raw vibration signal into the frequency domain signal and input it to the model.Then,Convolutional Neural Network(CNN)and Gate Recurrent Unit(GRU)are used to extract the depth spatiotemporal features of the bearing signal,the source and target domain feature is aligned using the maximum classifier discrepancy,and the orthogonal constraint is applied to constrain target domain features to increase the identifiability between features of unlabeled target domain feature.Finally,comparative experiments are conducted on the prediction of cross working condition RUL predict based on the bearing life dataset to evaluate the method in this work,and the optimal results are obtained in multiple experiments.