On the integration of domain knowledge and branching neural network for fatigue life prediction with small samples

A versatile data-driven model integrating domain knowledge and deep neural networks (DNNs) is proposed for fatigue life prediction with small samples. In the model, traditional fatigue life models, as comprehensive reflections of domain knowledge, are employed to generate pseudo labels for data augmentation. And a new DNN typology, called Branching neural network, is devised to distill useful training information without theoretical biases contamination. Moreover, further model improvement is achieved by the introduction of a subtractive clustering-based procedure for training data collection. The proposed model is experimentally validated in three case studies and shows better prediction performance against traditional models and conventional DNNs under small sample conditions.