Research on bearing fault diagnosis algorithm based on attention mechanism and dynamic convolutional neural network

In modern industrial production (such as wind power generation, automobile manufacturing, heavy-duty mining, etc.), bearings often operate for long periods of time in environments with variable loads and strong mechanical vibration noise. Once a fault is not easily detected early, it often leads to equipment shutdown, significantly increased maintenance costs, and even safety accidents. This paper proposes a bearing fault diagnosis algorithm based on Residual Network Multi-Scale Dynamic Convolutional Residual Network-Bidirectional Long Short-Term Memory + Multi head Self Attention (ResDARN-BiLSTM + Attention) to address the problem of insufficient feature extraction ability and low diagnostic accuracy of traditional fault diagnosis methods in such working conditions. The algorithm first uses a residual network with dynamic convolution to extract deep features from the raw vibration signals, followed by multi-scale dynamic convolution layers to capture multi-level information in the signals, thereby enhancing the feature representation capability. Additionally, an improved multi-head self-attention mechanism is introduced to strengthen the focus on key features. A bidirectional long short-term memory network is then used for temporal feature modeling, enabling precise classification of fault patterns. Finally, a classifier is employed for bearing fault diagnosis. The experimental results show that the proposed algorithm can achieve an accuracy of 100% in diagnosing performance on standard datasets without noise, significantly improving the accuracy and robustness of fault detection compared to traditional methods. The average accuracy of diagnostic performance in the presence of noise interference can also reach 92%. This model can extract and identify fault features online, providing feasible technical means for real-time monitoring and preventive maintenance of industrial production lines, helping to reduce equipment downtime losses and improve operation and maintenance efficiency. Compared with other fault diagnosis models, it has strong universality and provides a new technical direction for bearing fault detection and preventive maintenance.