A fault diagnosis method for few-shot industrial processes based on semantic segmentation and hybrid domain transfer learning.

Fault diagnosis of industrial processes plays an important role in avoiding heavy losses and ensuring production safety. Complex industrial processes often have many working conditions, and the actual industrial process often concentrates on certain working conditions. As a result, the running time of some working conditions is shorter, so the data of these conditions are difficult to obtain. However, almost all fault diagnosis methods based on Deep Learning (DL) requires a large amount of data. Therefore, it is a big challenge to realize the fault diagnosis of few-shot industrial processes. In order to solve these problems, this paper proposes a Deep Feature Transfer Fusion (DFTF) framework based on hybrid domain transfer learning. The purpose is to take few-shot working conditions as the target domain and carry out fault diagnosis for them. As the features of industrial process images are more complex, this paper introduces Pyramid Hybrid Vision Transformer (PHVT) model, which have stronger feature extraction capabilities and spatial perception, as feature extraction module. In order to improve the transferability of the model, this paper introduces the In-Cross Domain Hybrid Transfer Learning (ICTL) method. By fusing the general object features from ResNet50 which pre-trained under public dataset of common object and the features extracted from PHVT which pre-trained under industrial dataset of multiple working conditions, the adaptability of the model to different scenes is enhanced. The experimental results based on Pronto dataset of Process System Engineering lab of Cranfield University show that the proposed transfer learning method has excellent performance. [ABSTRACT FROM AUTHOR]