燕山大学学报2024，Vol.48Issue(1)：30-38,9.DOI:10.3969/j.issn.1007-791X.2024.01.004
基于卷积神经网络的抽油机故障诊断
Fault diagnosis of pumping unit based on convolutional neural network
摘要
Abstract
Pumping unit fault diagnosis is crucial to ensure the stable operation of oil and gas fields.The current fault diagnosis of pumping unit based on deep learning model has the problem that the number of parameters is too large,and it is difficult to be widely used in actual production.Considering the real demand for reducing system resource usage of the fault diagnosis model,a novel convolutional neural network is established based on dilated convolution and penalty mechanism in this study.In this model,dilated convolution residual blocks of different dilated convolution rates are deployed in the shallow neural network to efficiently acquire the contour features of the dynamometer card and reduce the number of model parameters.Moreover,the penalty mechanism is integrated into the Softmax loss function to enhance the influence of indistinguishable samples(such as gas influence)on the fault diagnosis model.Experimental validation is conducted with the data set made from actual working conditions of the pumping unit.When the accuracy rate is 96.6%,the number of parameters acquired by this model is 0.94 M,which is decreased by 3.30 M in MobileNetV3 model.Similarly,the floating-point operations calculated by this model is 165.24 M,which are also decreased by 52.22 M in MobileNetV3 model.In conclusion,the convolutional neural network holds potentially promising in the resource-constrained platform of actual production.关键词
卷积神经网络/抽油机/故障诊断/空洞卷积/损失函数Key words
convolutional neural network/pumping unit/fault diagnosis/dilated convolution/loss function分类
信息技术与安全科学引用本文复制引用
吴昊臻,许燕,周建平,谢欣岳,彭东..基于卷积神经网络的抽油机故障诊断[J].燕山大学学报,2024,48(1):30-38,9.基金项目
国家自然科学基金资助项目(52265061) （52265061）
新疆维吾尔自治区重点研发专项(2020B02016) （2020B02016）
