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  • CN 31-1691/TQ

基于并行LSTM-CNN的化工过程故障检测

肖飞扬 顾幸生

肖飞扬, 顾幸生. 基于并行LSTM-CNN的化工过程故障检测[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220120001
引用本文: 肖飞扬, 顾幸生. 基于并行LSTM-CNN的化工过程故障检测[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220120001
Xiao Feiyang, Gu Xingsheng. Fault detection of chemical process based on parallel connection PLSTM-CNN[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220120001
Citation: Xiao Feiyang, Gu Xingsheng. Fault detection of chemical process based on parallel connection PLSTM-CNN[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220120001

基于并行LSTM-CNN的化工过程故障检测

doi: 10.14135/j.cnki.1006-3080.20220120001
基金项目: 国家自然科学基金项目(61973120).
详细信息
    作者简介:

    肖飞扬(1997-),男,硕士生,研究方向为基于机器学习的故障诊断。E-mail:m17864291933@163.com

    通讯作者:

    顾幸生,E-mail:xsgu@ecust.edu.cn

  • 中图分类号: CN31-1691/TQ

Fault detection of chemical process based on parallel connection PLSTM-CNN

  • 摘要: 为了保证生产过程的安全稳定运行,避免因故障导致的损失,及时地检测出异常工况并准确地对异常工况进行诊断具有十分重要的研究意义。针对化工过程的复杂性,本文提出一种并行长短时记忆网络和卷积神经网络(Parallel long and short-term memory network and convolutional neural network,PLSTM-CNN)模型进行化工生产过程故障检测。该模型有效结合LSTM对时间序列数据全局特征提取能力和CNN模型擅于提取局部特征的能力,减少了特征信息的丢失,实现了较高的故障检测率。采用一维稠密卷积神经网络作为CNN的主体,结合LSTM网络对序列信息变化敏感的特点,在构建更深层网络的同时避免模型过拟合。采用了最大互信息(Maximum mutual information coefficient,MMIC)数据预处理方法,提高了数据的局部相关性,提高了PLSTM-CNN模型从不同初始条件下检测故障的效率。以田纳西州伊斯曼(Eastman Process of Tennessee,TE)过程为研究对象,PLSTM-CNN模型在故障平均检测率和漏报率等指标明显优于传统循环神经网络。

     

  • 图  1  卷积层

    Figure  1.  Convolution layer

    图  2  稠密网络结构图

    Figure  2.  Dense network structure diagram

    图  3  LSTM结构

    Figure  3.  LSTM structure

    图  4  基于PLSTM-CNN故障诊断方法

    Figure  4.  Fault diagnosis method based on PLSTM-CNN

    图  5  MI网格计算方法

    Figure  5.  MI grid calculation method

    图  7  变量排序后的格式

    Figure  7.  Format of the sorted variables

    图  6  正常数据与故障数据互信息值

    Figure  6.  Mutual information value of normal data and fault data

    图  8  PLSTM-CNN网络模型

    Figure  8.  PLSTM-CNN network model

    图  9  基于t-SNE的故障诊断可视化

    Figure  9.  Fault diagnosis visualization using t-SNE

    图  10  串行LSTM-CNN网络结构

    Figure  10.  Serial LSTM-CNN network structure

    图  11  平均故障检测率

    Figure  11.  Mean failure detection rate

    表  1  故障检测结果比较

    Table  1.   The comparison of fault detection results

    故障类型FDRFPR
    2D-CNNLSTMPLSTM-CNN2D-CNNLSTMPLSTM-CNN
    Normal 0.91 0.96 0.96 0.09 0.04 0
    Fault1 1.0 1.0 1.0 0 0 0
    Fault2 1.0 1.0 1.0 0 0 0
    Fault3 0.48 0.75 0.92 0.44 0.15 0.08
    Fault4 1.0 1.0 1.0 0 0 0
    Fault5 1.0 1.0 1.0 0 0 0
    Fault6 1.0 1.0 1.0 0 0 0
    Fault7 1.0 1.0 1.0 0 0 0
    Fault8 0.80 1.0 1.0 0.2 0 0
    Fault9 0.34 0.36 0.85 0.58 0.23 0.18
    Fault10 0.93 0.94 0.92 0.06 0.02 0.01
    Fault11 0.96 0.93 0.97 0.04 0.03 0.02
    Fault12 0.95 0.98 0.98 0.05 0.02 0.01
    Fault13 0.82 0.95 0.96 0.18 0.04 0.04
    Fault14 0.84 0.92 0.96 0.16 0.05 0.04
    Fault15 0.04 0.12 0.37 0.8 0.64 0.51
    Fault16 0.09 0.18 0.33 0.75 0.62 0.48
    Fault17 0.96 0.97 1.0 0.04 0.02 0.03
    Fault18 1.0 1.0 0.98 0 0 0
    Fault19 1.0 1.0 1.0 0 0 0
    Fault20 1.0 1.0 1.0 0 0 0
    Average 0.83 0.86 0.914 0.16 0.088 0.066
    下载: 导出CSV

    表  2  训练、推理时间比较

    Table  2.   Comparison of training and inference time

    ModelTraining time for
    one epochs(s)
    Reasoning time for
    one epochs(ms)
    1D-CNN2.5410
    2D-CNN65200
    LSTM312
    串联
    LSTM-CNN
    3.820
    并联
    LSTM-CNN
    4.225
    下载: 导出CSV

    表  3  小样本平均故障检测率

    Table  3.   Average failure detection rate of small samples

    ModelFDRFPR
    1D-CNN83.4%0.09
    2D-CNN78.4%0.07
    LSTM84.1%0.085
    串行LSTM-CNN84.9%0.12
    并行LSTM-CNN90.2%0.054
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-20
  • 网络出版日期:  2022-04-24

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