Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent years due to the promising development of artificial intelligent technology. Many intelligent diagnosis methods work well requiring massive historical data of the diagnosed object. However, it is hard to get sufficient fault data in advance in real diagnosis scenario and the diagnosis model constructed on such small dataset suffers from serious overfitting and losing the ability of generalization, which is described as small sample problem in this paper. Focus on the small sample problem, this paper proposes a new intelligent fault diagnosis framework based on dynamic model and transfer learning for rolling element bearings race faults. In the proposed framework, dynamic model of bearing is utilized to generate massive and various simulation data, then the diagnosis knowledge learned from simulation data is leveraged to real scenario based on convolutional neural network (CNN) and parameter transfer strategies. The effectiveness of the proposed method is verified and discussed based on three fault diagnosis cases in detail. The results show that based on the simulation data and parameter transfer strategies in CNN, the proposed method can learn more transferable features and reduce the feature distribution discrepancy, contributing to enhancing the fault identification performance significantly.

近年来，随着人工智能技术的蓬勃发展，滚动轴承的智能故障诊断越来越受到关注。许多智能诊断方法都需要大量的被诊断对象的历史数据才能很好地工作。然而，在实际诊断场景中，很难提前获得足够的故障数据，在如此小的数据集上构建的诊断模型存在严重的过拟合，失去泛化能力，本文将其描述为小样本问题。针对小样本问题，本文提出一种基于动态模型和迁移学习的滚动轴承滚道故障智能故障诊断框架。在所提出的框架中，轴承的动态模型被用来生成海量和各种模拟数据，然后基于卷积神经网络（CNN）和参数传递策略将从模拟数据中学到的诊断知识用于实际场景。基于三个故障诊断案例，对所提方法的有效性进行了详细的验证和讨论。结果表明，基于 CNN 中的仿真数据和参数传递策略，该方法可以学习更多可传递的特征并减少特征分布差异，有助于显着提高故障识别性能。