基于并行LSTM-CNN的化工过程故障检测

肖飞扬; 顾幸生

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

肖飞扬,
顾幸生^,

华东理工大学能源化工过程智能制造教育部重点实验室, 上海 200237

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: TP277
计量
- 文章访问数: 301
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- PDF下载量: 46
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-20
- 网络出版日期: 2022-04-24

Fault Detection of Chemical Process Based on Parallel LSTM-CNN

XIAO Feiyang,
GU Xingsheng^,

Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

摘要

摘要: 为保证生产过程的安全稳定运行，避免因故障导致损失，及时检测出异常工况并对异常工况进行准确诊断十分重要。针对化工过程的复杂性，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模型善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以及从不同初始条件下PLSTM-CNN模型检测故障的效率。以TE (Tennessee Eastman)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上明显优于传统循环神经网络。
- 故障检测 /
- 一维稠密卷积神经网络 /
- 长短时记忆网络 /
- 互信息 /
- TE过程
Abstract: In order to ensure the safe and stable operation of production processes and avoid losses caused by faults, it is quite important to detect abnormal working conditions in time and diagnose them accurately. Aiming at the complexity of chemical processes, this paper proposes a parallel long and short-term memory network and convolutional neural network (PLSTM-CNN) model for fault detection in chemical production process. By combining the LSTM's ability to extract global features from time series data and the CNN model's ability to extract local features, this model can effectively reduce the loss of feature information and achieve a high fault detection rate. Meanwhile, by using one-dimensional dense convolutional neural network as the main body of CNN and combining the LSTM network's sensitivity to sequence information changes, a deeper network can be built while avoiding model over fitting. Besides, the maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model in detecting faults under different initial conditions. Finally, it is shown from the experiment results on Tennessee Eastman (TE) process that the PLSTM-CNN model is obviously superior to the traditional recurrent neural network in such indicators as average failure detection rate and false negative rate.
- fault detection /
- one-dimensional dense convolutional neural network /
- long and short-term memory network /
- mutual information /
- TE process

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图 1 维卷积操作

Figure 1. One-dimensional convolution operation

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图 2 稠密网络结构图

Figure 2. Dense network structure diagram

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图 3 LSTM结构

Figure 3. LSTM structure

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图 4 基于PLSTM-CNN故障诊断方法

Figure 4. Fault diagnosis method based on PLSTM-CNN

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图 5 MI网格计算方法

Figure 5. MI grid calculation method

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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

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图 7 变量排序后的格式

Figure 7. Sorted variable order

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图 8 PLSTM-CNN网络模型

Figure 8. PLSTM-CNN network model

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图 9 基于t-SNE的故障诊断可视化

Figure 9. Fault diagnosis visualization base on t-SNE

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图 10 串行LSTM-CNN网络结构

Figure 10. Serial LSTM-CNN network structure

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图 11 平均故障检测率

Figure 11. Average fault detection rate

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表 1 故障检测结果比较

Table 1. Comparison of fault detection results

Type	FDR			FPR
Type	2D-CNN	LSTM	PLSTM-CNN	2D-CNN	LSTM	PLSTM-CNN
Normal	0.91	0.96	0.96	0.09	0.04	0
Fault1	1.00	1.00	1.00	0	0	0
Fault2	1.00	1.00	1.00	0	0	0
Fault3	0.48	0.75	0.92	0.44	0.15	0.08
Fault4	1.00	1.00	1.00	0	0	0
Fault5	1.00	1.00	1.00	0	0	0
Fault6	1.00	1.00	1.00	0	0	0
Fault7	1.00	1.00	1.00	0	0	0
Fault8	0.80	1.00	1.00	0.20	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	0.80	0.64	0.51
Fault16	0.09	0.18	0.33	0.75	0.62	0.48
Fault17	0.96	0.97	1.00	0.04	0.02	0.03
Fault18	1.00	1.00	0.98	0	0	0
Fault19	1.00	1.00	1.00	0	0	0
Fault20	1.00	1.00	1.00	0	0	0
Average	0.83	0.86	0.91	0.16	0.08	0.06

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表 2 训练、推理时间比较

Table 2. Comparison of training and reasoning time

Model	Training time for one epoch/s	Reasoning time for one epoch/ms
1D-CNN	2.54	10
2D-CNN	65.00	200
LSTM	3.00	12
CLSTM-CNN	3.80	20
PLSTM-CNN	4.20	25

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表 3 小样本平均故障检测率

Table 3. Average fault detection rate of small samples

Model	FDR	FPR
1D-CNN	0.83	0.09
2D-CNN	0.78	0.07
LSTM	0.84	0.09
CLSTM-CNN	0.85	0.12
PLSTM-CNN	0.90	0.05

下载: 导出CSV

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