吉林大学学报（信息科学版）2025，Vol.43Issue(5)：978-987,10.
基于多模态决策融合的抽油机故障诊断方法
Pumping Unit Fault Diagnosis Method Based on Multimodal Decision Fusion
摘要
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(Shuffle Net V2 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'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-MLP model is only 10.16 MByte,and the fault diagnosis accuracy reaches 95.35％,which better meets the needs of pumping unit fault diagnosis.关键词
示功图/ShuffleNetV2模型/多层感知机/注意力机制/故障诊断/多模态Key words
indicator diagram/ShuffleNetV2 model/multi-layer perceptron(MLP)/attention mechanism/fault diagnosis/multimodality分类
计算机与自动化引用本文复制引用
张强,薛冰,王伯超,陈诚,陆俊翼..基于多模态决策融合的抽油机故障诊断方法[J].吉林大学学报（信息科学版）,2025,43(5):978-987,10.基金项目
国家自然科学基金资助项目(42002138) （42002138）
黑龙江省自然科学基金资助项目(LH2022F008) （LH2022F008）
黑龙江省博士后专项基金资助项目(LBH-Q20077) （LBH-Q20077）
黑龙江省优秀青年教师基础研究支持计划基金资助项目(YQJH2023073) （YQJH2023073）
