Multiattention-Based Feature Aggregation Convolutional Networks With Dual Focal Loss for Fault Diagnosis of Rotating Machinery Under Data Imbalance Conditions

Convolutional neural network (CNN)-based intelligent fault diagnosis approaches have showcased remarkable performance in the assessment of machine safety. The data monitored from mechanical systems in industries is primarily characterized by class imbalance. Nevertheless, most of the current CNN-based models are designed under the assumption of balanced sample distributions, which do not align with the prevalent conditions observed in real industrial scenarios. To tackle this challenge, a state-of-the-art multiattention-based feature aggregation convolutional network (MFACN) is developed in this study. The key contributions of this study are outlined as follows: 1) this study designs an attention-based multiscale module (AMM) and a multiscale feature aggregation module (MFAM) to facilitate comprehensive learning across multiple levels; 2) a robust CNN model based on AMM and MFAM is established. The constructed model can explore abundant information from mechanical signals; and 3) a dual focal loss (DFL) function is introduced to enhance diagnostic results under conditions of data imbalance. To assess the applicability of the proposed MFACN in machine health state identification, two experiments were conducted using the bearing dataset and the planetary gearbox dataset. The experimental results unequivocally show that MFACN surpasses seven other state-of-the-art approaches, especially when dealing with imbalanced datasets.