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    何雨旻, 侍洪波. 基于多块卷积变分信息瓶颈的多变量动态过程故障诊断[J]. 华东理工大学学报(自然科学版), 2021, 47(6): 716-725. DOI: 10.14135/j.cnki.1006-3080.20201022001
    引用本文: 何雨旻, 侍洪波. 基于多块卷积变分信息瓶颈的多变量动态过程故障诊断[J]. 华东理工大学学报(自然科学版), 2021, 47(6): 716-725. DOI: 10.14135/j.cnki.1006-3080.20201022001
    HE Yumin, SHI Hongbo. Multivariate Dynamic Process Fault Diagnosis Based on Multi-Block Convolutional Variational Information Bottleneck[J]. Journal of East China University of Science and Technology, 2021, 47(6): 716-725. DOI: 10.14135/j.cnki.1006-3080.20201022001
    Citation: HE Yumin, SHI Hongbo. Multivariate Dynamic Process Fault Diagnosis Based on Multi-Block Convolutional Variational Information Bottleneck[J]. Journal of East China University of Science and Technology, 2021, 47(6): 716-725. DOI: 10.14135/j.cnki.1006-3080.20201022001

    基于多块卷积变分信息瓶颈的多变量动态过程故障诊断

    Multivariate Dynamic Process Fault Diagnosis Based on Multi-Block Convolutional Variational Information Bottleneck

    • 摘要: 针对多变量动态过程的故障诊断,采用局部提取、全局整合的特征提取策略,提出了一种多块卷积变分信息瓶颈(Multi-Block Convolutional Variational Information Bottleneck,MBCVIB)模型。首先,根据过程机理,对所有变量分块,将同一操作单元的变量划分为同一子块,再利用一维卷积神经网络(One-Dimensional Convolutional Neural Network,1-D CNN)提取过程中每个子块的局部特征,从而考虑样本间的时序相关性;然后,整合局部特征得到全局特征表示,在全局特征的基础上,根据变分信息瓶颈(Variational Information Bottleneck,VIB)原理进一步提取与故障最相关的信息;最后,采用连续搅拌釜反应器(Continuous Stirred Tank Reactor,CSTR)和田纳西-伊士曼过程(Tennessee Eastman Process,TEP)对模型的有效性进行了验证。结果显示本文模型在CSTR上实现了0.983的平均故障诊断准确率,在TEP上实现了0.955的平均故障诊断准确率。

       

      Abstract: By using the feature extraction strategy of local extraction and global integration, this paper proposes a multi-block convolutional variational information bottleneck (MBCVIB) for the fault diagnosis of multivariate dynamic processes. Firstly, according to the process mechanism, all variables are divided into sub-blocks and the variables in the same operation unit will be put into the same block. Secondly, one-dimension convolutional neural network (1-D CNN) is used to extract the local dynamic features of each operating unit in the process, which considers the temporal correlation between samples. Besides, a global feature representation is constructed by concatenating the local dynamic features of all operating units. On the basis of global features, the most relevant fault information is further extracted according to the variational information bottleneck principle. Finally, the proposed model is validated via Continuous Stirred Tank Reactor (CSTR) and Tennessee Eastman Process (TEP), which achieves an average fault diagnosis accuracy of 0.983 on CSTR and 0.955 on TEP.

       

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