A novel multiscale hybrid neural network for intelligent fine-grained fault diagnosis

Various intelligent methods for condition monitoring and fault diagnosis of mechanical equipment have been developed over the past few years. However, most of the existing deep learning (DL)-based fault diagnosis models perform well only when applied to deal with limited types of general failures, and these models fail to accurately distinguish fine-grained faults under multiple working conditions. To address these challenges, we propose a novel multiscale hybrid model (MSHM), which takes the raw vibration signal as input and progressively learns representative features containing both spatial and temporal information to effectively classify fine-grained faults in an end-to-end way. To simulate fine-grained failure scenarios in practice, more than 100 classes of faults under different working conditions are constructed based on two benchmark datasets, and the experimental results demonstrate that our proposed MSHM has advantages over state-of-the-art methods in terms of accuracy in identifying fine-grained faults, generality in handling fault classes of different granularity, and learning ability with limited data.