Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Abstract

Variable speed is one of the common working conditions of mechanical equipment,which poses an important challenge to equipment fault diagnosis. The current solutions have the shortcomings of low computational efficiency and large diagnostic errors. The ability of attention mechanism to automatically extract useful features has begun to attract widespread attention in the field of mechanical intelligent fault diagnosis. Combining the advantages of attention mechanism and unsupervised learning, this paper proposes a squeeze-excitation attention guided sparse filtering (SESF) method for mechanical intelligent fault diagnosis method under variable speed. Firstly, the SE attention mechanism is embedded in SF algorithm to guide model training. Then, unsupervised feature extraction is carried out on the variable speed signal samples. The training results are adaptively screened and weighted to make the model pay more attention to the region with the most classify discrimination, so as to improve the feature extraction ability of the model to obtain useful information. Finally, two sets of gear and bearing tests under variable speed condition are adopted to testify the performance of the proposed method. The experimental results show that the SESF method can overcome the influence of variable speed to achieve accurate recognition of different mechanical faults and is superior to the other methods.

A noise-robust CNN architecture with global attention and gated convolutional Kernels for bearing fault detection

“…Attention mechanisms have revolutionized the way neural networks process information by enabling models to focus on specific parts of the input data, improving efficiency and performance across tasks such as classification, prediction and beyond UrRehman et al (2024). In recent years, an increasing number of empirical studies have demonstrated the significant role of incorporating attention mechanisms into bearing fault diagnosis Yao et al (2023), Dong et al (2024), Han et al (2024). This approach allows the model to selectively focus on key data points, thereby enhancing its ability to identify important fault characteristics with greater accuracy.…”