Rotating machinery fault diagnosis has significant importance in the industrial domain.This research presents a novel approach for diagnosing faults in rotating machinery using a combination of coordinate attention mechanism and transfer learning.The method involves converting the raw signals from the rotating machinery into time-frequency representations using continuous wavelet transform,enabling the identification of fault patterns in the time-frequency domain.Then,a model based on the coordinate attention mechanism is introduced,which adaptively learns the feature weights at different positions,enhancing the discriminative power of fault features.By training the network in both pre-training and fine-tuning stages,the model achieves transfer learning across different operating conditions,improving its generalization capability.Experimental results demonstrate a significant performance improvement of this method in rotating machinery fault diagnosis.Compared to traditional fault diagnosis methods,the model based on the coordinate attention mechanism achieves notable improvements in fault recognition accuracy.Moreover,through transfer learning,the model exhibits good performance across different operating conditions,demonstrating its generalization ability and adaptability.
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