An Attention-Based Multidimensional Fault Information Sharing Framework for Bearing Fault Diagnosis.

Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information. Aiming at the above problems, this paper proposes an Attention-based Multidimensional Fault Information Sharing (AMFIS) framework, which aims to overcome the difficulties of multidimensional bearing fault diagnosis in a small sample environment. Specifically, firstly, a shared network is designed to capture the common knowledge of the Fault Localization Task (FLT) and the Fault Quantification Task (FQT) and save it to the global feature pool. Secondly, two branching networks for performing FLT and FQT were constructed, and an attentional mechanism (AM) was used to filter out features from the shared network that were more relevant to the task to enhance the branching network's capability under small samples. Meanwhile, we propose an innovative Dynamic Adjustment Strategy (DAS) designed to adaptively regulate the training weights of FLT and FQT tasks to achieve optimal training results. Finally, extensive experiments are conducted in two cases to verify the effectiveness and superiority of AMFIS.