Rolling bearing fault diagnosis model based on external attention integrated convolutional neural network under imbalanced data conditions

Abstract Rolling bearings are essential components in numerous mechanical systems, and their failure can result in considerable downtime and expensive repairs. Therefore, accurate and timely fault diagnosis is vital for effective predictive maintenance and overall reliability. Traditional diagnostic methods often struggle with complex and non-stationary signals, compounded by issues of data imbalance in real-world scenarios. A method for diagnosing rolling bearing faults has been developed in this paper utilizing external attention (EA), convolutional neural networks (CNN), and continuous wavelet transform (CWT), specifically addressing the challenge of imbalanced sample data. This approach offers significant advantages, including a reduction in complexity by eliminating the need for data augmentation and leveraging EA for enhanced feature extraction from samples. Compared to other attention mechanisms, this method demonstrates outstanding performance on both training and testing sets with imbalanced samples, exhibiting minimal overfitting tendencies. The proposed CWT-EACNN method effectively addresses the challenge of imbalanced sample data in rolling bearing fault diagnosis, demonstrating exceptional performance and reduced complexity.