关键词: 故障诊断, 卷积神经网络, 长短时记忆, 注意力机制, 深度学习

Abstract: Aiming at the problems of complex feature extraction， large amount of model parameters and low diagnostic efficiency in traditional fault diagnosis methods of pumping unit based on dynamometer diagram， this paper proposes a fault diagnosis method based on 1D-CNN-LSTM attention network. The dynamometer diagram is converted into a load displacement sequence as the network input， the one-dimensional convolutional neural network （1D-CNN） is used to extract local features of the sequence while reducing sequence length. Considering the temporal characteristics of the sequence， the long-short-term memory （LSTM） network is further used to extract temporal features of the sequence. In order to highlight the impact of key features， the attention mechanism is introduced to give higher attention weights to temporal features related to fault type. Finally， the weighted features are input into a fully connected layer， and the Softmax classifier is used to realize fault diagnosis. The experimental results show that the average accuracy， precision， recall and F1 value of the proposed method reach 99.13%， 99.35%， 99.17% and 99.25%， respectively， and the model size is only 98 kB. Compared with other methods based on feature engineering， it has higher diagnostic accuracy and generalization. Compared with other methods based on two-dimensional convolutional neural network （2D-CNN） model， it significantly reduces model parameters and training time， improves the efficiency of fault diagnosis.

Key words: fault diagnosis, convolutional neural network, long short-term memory, attention mechanism, deep learning