基于W-DenseNet的减压阀不平衡样本故障诊断模型
作者:
作者单位:

1. 江南大学 机械工程学院,江苏 无锡 214122;2. 江南大学 江苏省食品先进制造装备技术重点实验室,江苏 无锡 214122

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通讯作者:

E-mail: zhanghong@jiangnan.edu.cn.

中图分类号:

TP277

基金项目:

江苏省“六大人才高峰”计划项目(ZBZZ-012);高等学校学科创新引智计划项目(B18027);江苏省高校优秀科技创新团队基金项目(2019SJK07).


W-DenseNet-based fault diagnosis model of pressure-reducing valve with unbalanced samples
Author:
Affiliation:

1. School of Mechanical Engineering,Jiangnan University,Wuxi 214122,China;2. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology,Jiangnan University,Wuxi 214122,China

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    摘要:

    针对实际工况中被测对象大多处于正常状态而引起故障样本稀缺、故障数据间存在差异,导致故障类别识别准确率不高的问题,基于密集卷积神经网络(DenseNet),提出一种减压阀样本数据不平衡下的故障诊断模型-----加权密集卷积神经网络(W-DenseNet).首先,将原始一维压力信号数据重构后转换为二维灰度图,作为模型的输入数据;其次,以DenseNet为基础框架搭建特征提取网络;然后,在损失函数中为不同类别样本添加惩罚系数以实现不平衡样本误差的加权平均;最后,为验证模型的有效性,搭建减压阀数据采集系统并进行分类性能实验.实验结果表明:W-DenseNet模型在不同平衡度的减压阀数据集下均有良好的分类效果,且当各故障类间均存在样本不平衡现象时,模型对3种故障类型的召回率仍分别高达95.18%、95.47%、96.89%.

    Abstract:

    Considering the fact that most of the tested objects are in the normal state under practical working conditions can cause the scarcity of fault samples and differences within fault data, which further leads to low accuracy of fault classification, a DenseNet-based fault diagnosis model of unbalanced samples for the pressure-reducing valve is proposed, called weighted dense convolutional neural network(W-DenseNet). Firstly, the input data of the model is obtained by reconstructing original data of one-dimensional pressure signal and converting it into a two-dimensional grayscale image. Next, a feature extraction network is built based on the DenseNet. Then, to realize the weighted average of unbalanced sample errors, penalty coefficients are added to different types of samples in the loss function. Finally, the data acquisition system of the pressure-reducing valve is built and the classification performance experiment is carried out to validate the proposed model. The experimental results show that the W-DenseNet model exhibits good classification performance on data sets of pressure-reducing valves with different degrees of balance. When the sample imbalance occurs among each fault class, the recall rate of the model for the three fault types is still up to 95.18%, 95.47%, and 96.89%, respectively.

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张洪,盛永健,黄子龙,等.基于W-DenseNet的减压阀不平衡样本故障诊断模型[J].控制与决策,2022,37(6):1513-1520

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  • 在线发布日期: 2022-04-22
  • 出版日期: 2022-06-20