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Early fault identification of high-pressure water pumps based on multi-attention mechanism

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This method demonstrates early fault identification in high-pressure pumps, suggesting improved accuracy with MAMSCNN-LSTM.

The proposed MAMSCNN-LSTM model achieves an impressive average fault identification accuracy of 97.57%.
Vibration signals are adaptively analyzed using a Multi-Scale Convolutional Neural Network for improved feature extraction.
Integrating a Multi-Head Attention mechanism enhances feature fusion and model generalization for fault diagnosis.
The model improves fault identification by capturing temporal dependencies with a Long Short-Term Memory network.

Zhang et al. (2025) studied this question.

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