An information fusion-based meta transfer learning method for few-shot fault diagnosis under varying operating conditions

In recent years, meta-learning has gained increasing attention in the field of fault diagnosis due to its advantages of handling small samples and exhibiting fast adaptation across different diagnostic tasks. However, the scenario of sharply varying operating conditions and the heavy computation burden still limit the effective application of meta-learning in the field of transfer fault diagnosis. To address the above challenges, a few-shot meta transfer diagnosis method is proposed based on information fusion-based model agnostic meta-learning (IFMAML). Firstly, the information enhancement method based on sparse principal component analysis is introduced to enhance the domain invariant features and reduce data redundancy. Subsequently, the information fusion strategy of multiple sensor data is proposed to form the red, green, and blue (RGB) channel information of images, which can enrich the diversity of domain invariant features and mine the spatial information of multiple sensors. Then, the IFMAML, with its enhanced potential for diagnostic performance and computational efficiency, is developed to address the challenging few-shot cross-domain transfer diagnosis under varying operating conditions. Finally, two case studies for gearbox fault diagnostics considering sharply varying speed conditions and unknown health conditions have been conducted to demonstrate the effectiveness and superiority of the proposed method. Experimental results have indicated that the proposed IFMAML method can achieve superior diagnostic accuracy and can be quickly adapted to new transfer diagnosis scenarios under varying operating conditions, when compared with several mainstream meta-learning methods.