基于改进残差网络的抽油机故障诊断研究

Research on Fault Diagnosis of Oil Pump Based on Improved Residual Network

针对抽油机故障诊断的传统图像识别方法识别率高但速率较慢,或训练速度适宜但识别率较低等问题,提出一种基于改进残差网络模型的示功图图像识别算法。改进策略包括替换模型第1层卷积核,由更小卷积核代替;改变残差模块排列顺序;将传统ResNet50(残差网络)模型的全连接层替换成径向基函数(RBF:Radial Basis Function)网络作为额外的分类器;采用数据增强方式对数据集进行扩充,并利用迁移学习在改进的ResNet50-RBF模型得到ImageNet上预训练好的权重参数后进行训练。实验结果表明,改进的模型在示功图识别中得到了98.86%的准确率,与其他网络相比,鲁棒性进一步加强,并且速率得到一定提升,为抽油机故障诊断提供了一定参考。

A novel approach is proposed to address the issues of high accuracy but slow speed or low accuracy but appropriate training speed in traditional image recognition methods for fault diagnosis of oil pumps.The proposed method is based on an enhanced residual network model,with several improvement strategies.Firstly,the first-layer convolution kernel of the model is replaced with a smaller one.Secondly,the order of residual modules is changed.Thirdly,the fully connected layer of ResNet50(a Residual Network model)is replaced with an RBF(Radial Basis Function)network as an additional classifier.Finally,data augmentation techniques are used to expand the dataset,and transfer learning is utilized to obtain pre-trained weight parameters on ImageNet for the improved ResNet50-RBF model.Experimental results demonstrate that the proposed model achieves 98.86%accuracy in pump curve recognition,exhibiting stronger robustness and improved speed compared to other networks.This provides some reference for fault diagnosis of oil pumps.The proposed method can significantly enhance the efficiency and accuracy of image recognition in fault diagnosis for oil pumps,which is of great significance for practical applications in the industry.