基于W-DenseNet的减压阀不平衡样本故障诊断模型
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江南大学

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TP277;TH89

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高等学校学科创新引智计划资助项目(B18027);江南大学研究生科研与实践创新计划项目(JNSJ19_005)资助


Unbalanced Sample Fault Diagnosis Model of Pressure Reducing Valve Based on W-DenseNet
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Jiangnan University

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    摘要:

    针对实际工况中被测对象多处于正常状态而导致故障样本稀缺、故障类别识别准确率不高的问题,基于深度学习理论,提出了一种将DenseNet和加权交叉熵损失函数相结合的故障诊断模型,实现了不平衡样本的故障诊断。首先,介绍了密集卷积神经网络模型;其次,在损失函数中为不同类别样本添加惩罚系数实现不平衡样本误差的加权平均;然后,结合两者提出了基于DenseNet的不平衡样本故障诊断模型W-DenseNet;最后,为验证模型的有效性,使用不同平衡度的减压阀数据集进行了分类性能实验,并与传统卷积神经网络和密集卷积神经网络进行了对比验证。实验结果表明:W-DenseNet模型相较于其他两种模型在不降低整体分类准确率的前提下能显著提升少数类样本的分类准确率。

    Abstract:

    In order to solve the problem that most of the tested objects are in normal state, the fault samples are scarce and the accuracy of fault classification is not high, on the basis of deep learning theory, A fault diagnosis model based on DenseNet and weighted loss function is proposed, The fault diagnosis of unbalanced samples is realized. First, the dense convolution neural network model is introduced. Then, in the loss function, penalty coefficients are added to different types of samples to realize the weighted average of unbalanced sample errors. Furthermore, combining advantages of both the DenseNet and weighted loss function, a novel network architecture, W-DenseNet is proposed in this paper. Finally, to verify the effectiveness of the model, the classification performance of the pressure reducing valve data sets with different balance degrees is tested and compared with the traditional convolution neural network and dense convolution neural network. The experimental results show that the proposed model can significantly improve the classification accuracy of a small number of samples without reducing the overall classification accuracy.

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  • 收稿日期:2020-12-08
  • 最后修改日期:2021-04-01
  • 录用日期:2021-04-07
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