Bearing compound fault diagnosis considering the fusion fragment data and multi-head attention mechanism considering the actual variable working conditions

In this paper, a bearing compound fault diagnosis model considering the actual variable working conditions, which combines segment data and multi head attention mechanism, is proposed to improve the accurate recognition ability of compound fault signals. The design of the overall model architecture, which combines the advantages of the convolution layer and the multi-head attention layer, enables the model to better handle fragmented compound fault signals under multiple conditions in engineering practice. In addition, the application strategies under different working conditions are also discussed to ensure that the model has good robustness in the real environment. Through a series of experiments, the excellent diagnostic performance of the proposed model under different working conditions and noise environment is demonstrated. Compared with other existing models, the results showed that the proposed model not only improves the accuracy of fault diagnosis but also demonstrated excellent industrial field adaptability and stability. This research not only provides a new perspective and methodology for the field of fault diagnosis, but also provides a technical basis for industrial intelligence and digital transformation, which has a broad application prospect and value.