摘要
针对柱塞泵故障诊断中存在的振动数据不平稳和诊断精度低问题,提出一种考虑传感器位置信息的Bagging集成卷积神经网络(Sensors location considered-bagging-convolution neural network,SLC-B-CNN)故障诊断模型。首先将短时傅里叶变换后的二维振动时频信息与经独热编码后的传感器位置信息匹配以构建数据集,然后设计混合双输入CNN模型作为基分类器,最后将数据集输入以简单平均法聚合的SLC-B-CNN模型中,在实验柱塞泵数据集上验证了所提出的SLC-B-CNN模型,在测试集上的准确率高达92%,各类故障平均召回率为89%,表现优于CNN模型和随机森林(Random forest,RF)模型。
Aiming at the problems of unstable vibration data and low diagnostic accuracy in the fault diagnosis of piston pumps,a fault diagnosis model of sensors location considered-Bagging-convolution neural network(SLC-B-CNN)is proposed.Firstly,the two-dimensional vibration time-frequency information after short time Fourier transform is matched with the sensors location information encoded by one-hot to construct the dataset.Then,the dual-input hybrid CNN model is designed as the base classifier.And finally the dataset is put into the SLC-B-CNN model that is aggregated by simple averaging.The proposed SLC-B-CNN model is verified on the experimental piston pump dataset,with an accuracy rate of 92%on the test set and an average recall rate of 89%for various faults.The performance is better than the CNN model and the random forest model.
作者
滕建强
罗风
张菁
张玉涛
夏唐斌
TENG Jianqiang;LUO Feng;ZHANG Jing;ZHANG Yutao;XIA Tangbin(Sinopec Northwest China Petroleum Bureau,Wulumuqi Xinjiang 830011,China;State Key Laboratory of Mechanical System and Vibration,School of Mechanical Engineering,Shanghai Jiao Tong University,SJTU-Fraunhofer Center,Shanghai 200240,China;Petromentor Intelligence Safe Technology(Beijing)Co.,Ltd.,Beijing 100012,China)
出处
《机械设计与研究》
CSCD
北大核心
2024年第4期220-225,共6页
Machine Design And Research
基金
中国石化重大科技项目(P18022)。
关键词
柱塞泵
故障诊断
卷积神经网络
集成学习
传感器位置信息
piston pump
fault diagnose
convolutional neural network
ensemble learning
sensors location
作者简介
滕建强(1985-),男,工程师,主要研究方向:仪表自动化,已发表论文4篇。E-mail:505375146@qq.com;通讯作者:张玉涛。
