Bearing fault diagnosis based on Kepler algorithm and attention mechanism

As a crucial component in rotating machinery, bearings are prone to varying degrees of damage in practical application scenarios. Therefore, studying the fault diagnosis of bearings is of great significance. This article proposes the Kepler algorithm to optimize the weights of neural networks and improve the diagnostic accuracy of the model. At the same time, combined with attention mechanisms, the model will focus on useful information, ignore useless information, and efficiently extract key features. Finally, using third-party bearing data and inputting it into the fault diagnosis model, it was verified that Kepler algorithm and attention mechanism can improve the diagnostic accuracy. Meanwhile, the algorithm proposed in this paper was compared with other algorithms to verify its feasibility and superiority.