Deep guided wave convolution neural network committee-based multi-path fusion diagnosis method for fatigue corner crack

Accurate diagnosis of crack size is a critical task for guided wave (GW)-based structural health monitoring (SHM). However, fatigue cracks would have complex morphology due to complex structural geometries and loading conditions, in which multiple dimension characteristics, like crack length, depth, and angle are involved. It is challenging to quantitatively evaluate these characteristics with GW signals from a single excitation-sensing path. This paper proposes a novel deep guided wave convolution neural network (CNN) committee-based multi-path GW fusion diagnosis method, aiming at quantitative evaluation of dimension characteristics of the complex fatigue damage. GW signals from multiple excitation-sensing paths are synthesized as a high-dimension input image to enhance the effects of the fatigue crack. Besides, the deep GW-CNN committee is developed for damage quantification, in which each GW-CNN is trained with a portion of the training dataset to reduce the impact of small sample size. The proposed method is validated on fatigue tests of landing gear beam specimens under variable amplitude loading, which is designed referring to the critical region of a real aircraft and its fatigue crack presents as a corner crack. The leave-one-out validation results show the effectiveness of the proposed method, especially improvements in the diagnosis of small cracks.