Abstract: Aiming at the problem that most of the existing pumping unit fault diagnosis is based on indicator diagram, which leads to a relatively single diagnostic modality, a ShuffleNetV2ECA-MLP (ShuffleNetV2 with Efficient Channel Attention and Multilayer Perceptron, ShuffleNetV2ECA-MLP) multimodal decision fusion fault diagnosis model is proposed for pumping units. In order to improve the cross-channel interaction capability and recognition accuracy of the ShuffleNetV2 model, firstly, the ECA(Efficient Channel Attention) module with lightweight channel attention is introduced into the ShuffleNetV2 model, and the Hardswish activation function is applied to enhance the network蒺s ability to learn complex problems. Secondly, the improved ShuffleNetV2 network is used to diagnose the figure of merit, and the MLP(Multi-Layer Perceptron) network is used to process the production dynamic data. Finally, the diagnostic results of the two models are integrated using the weighted voting method. In order to verify the effectiveness of the improved ShuffleNetV2 and ShuffleNetV2ECA-MLP models, comparisons are made with the lightweight convolutional networks MobileNetV2, MobileNetV3, the classical convolutional network ResNet, and the VGG ( Visual Geometry Group) network model. The experimental results show that the storage space of the ShuffleNetV2ECA-MLPmodel is only 10. 16 MByte, and the fault diagnosis accuracy reaches 95. 35% , which better meets the needs of pumping unit fault diagnosis.

Key words: indicator diagram, ShuffleNetV2 model, multi-layer perceptron(MLP), attention mechanism, fault diagnosis, multimodality