基于稀疏过滤特征学习的化工过程故障检测方法

江升; 旷天亮; 李秀喜

doi:10.11949/0438-1157.20190894

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基于稀疏过滤特征学习的化工过程故障检测方法

过程系统工程 | 更新时间：2026-07-24

- 基于稀疏过滤特征学习的化工过程故障检测方法
- A chemical process fault detection method based on sparse filtering feature learning
- 化工学报 2019年70卷第12期页码：4698-4709
- 作者机构：
  
  华南理工大学化学与化工学院，广东广州 510640
- 作者简介：
  
  江升（1994—），男，硕士研究生，1339625174@qq.com
  李秀喜（1966—），男，博士，教授，cexxli@scut.edu.cn
- 基金信息：
- DOI：10.11949/0438-1157.20190894
  中图分类号： TQ 021.8
- 收稿：2019-08-11，
  
  修回：2019-08-19，
  
  网络首发：2020-06-09，
  
  纸质出版：2019-12-05
- 稿件说明：
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江升，旷天亮，李秀喜. 基于稀疏过滤特征学习的化工过程故障检测方法[J]. 化工学报, 2019, 70(12): 4698-4709

JIANG Sheng, KUANG Tianliang, LI Xiuxi. A chemical process fault detection method based on sparse filtering feature learning[J]. CIESC Journal, 2019, 70(12): 4698-4709
江升，旷天亮，李秀喜. 基于稀疏过滤特征学习的化工过程故障检测方法[J]. 化工学报, 2019, 70(12): 4698-4709 DOI： 10.11949/0438-1157.20190894.

JIANG Sheng, KUANG Tianliang, LI Xiuxi. A chemical process fault detection method based on sparse filtering feature learning[J]. CIESC Journal, 2019, 70(12): 4698-4709 DOI： 10.11949/0438-1157.20190894.

摘要

过程安全一直以来是化学工业中尤为重要的问题之一，故障检测与诊断（FDD）作为化工异常工况管理最有力的工具之一，给过程安全提供了保障。随着深度学习的发展，很多智能学习算法已经被提出，然而这些算法却很少被应用到FDD中来。提出了一种基于稀疏过滤和逻辑回归（SFLR）算法的化工过程故障检测新方法。采用TE过程和环己烷无催化氧化制环己酮过程对提出的方法进行了验证，结果表明，所提出的方法均具有较高的诊断精度，案例研究表明提出的方法可以及时有效地诊断出故障。

Abstract

Process safety has always been one of the most important issues in the chemical industry

fault detection and diagnosis(FDD) is one of the most powerful tools for chemical process abnormal events management

which provides guarantee for process safety. With the development of deep learning

many intelligent learning algorithms have been proposed

but these algorithms are rarely applied to FDD. In this paper

a novel chemical process fault diagnosis method is proposed based on sparse filtering and logistic regression (SFLR) .The main idea of this method is to divide chemical process raw data into training data and test data firstly

then standardized and whitening preprocessing

and then train sparse filtering(SF) model with three layers of neural networks

and use the SF model for unsupervised feature learning. Finally

logistic regression(LR) model with supervised learning is used to classify the chemical process health conditions using learned features. The proposed method was verified by the TE process and the non-catalytic oxidation of cyclohexane to cyclohexanone. The results show that the proposed method has high diagnostic accuracy

and the case study shows that the proposed method can diagnose faults in a timely and effective manner.

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references

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