青岛科技大学化工学院，山东 青岛 266042
收稿日期:2017-09-29修回日期:2017-11-24出版日期:2018-06-22发布日期:2018-06-06通讯作者:田文德基金资助:基于非线性动态模型的精馏过程安全智能预测方法与预警策略研究Application of Fault Classification Method Based on VAE-DBN in Chemical Process
Xiang ZHANG, Zhe CUI, Yuxi DONG, Wende TIAN*College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao, Shandong 266042, China
Received:2017-09-29Revised:2017-11-24Online:2018-06-22Published:2018-06-06Contact:TIAN Wen-de 摘要/Abstract
摘要： 针对化工过程高维数据的故障特征难以提取的难题，提出变分自动编码器(VAE)结合深度置信网络(DBN)的混合故障诊断方法. 在VAE的编码部分对隐变量空间Z添加约束，通过重参数化方法进行反向传播训练，可无监督地学习不同故障对应的隐变量特征，其作为DBN分类模型的输入特征训练网络，输入测试集进行故障诊断. 田纳西伊斯曼流程(TE)应用结果表明，VAE能提取原始数据更加抽象有效的特征，VAE?DBN分类准确.
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张祥 崔哲 董玉玺 田文德. 基于VAE-DBN的故障分类方法在化工过程中的应用[J]. 过程工程学报, 2018, 18(3): 590-594.
Xiang ZHANG Zhe CUI Yuxi DONG Wende TIAN. Application of Fault Classification Method Based on VAE-DBN in Chemical Process[J]. Chin. J. Process Eng., 2018, 18(3): 590-594.
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