面向工业混杂系统故障检测的扩展数据逻辑分析方法

孙中建; 杨博; 齐楚; 李宏光

doi:10.11949/0438-1157.20200328

您当前的位置：

首页 >

文章列表页 >

面向工业混杂系统故障检测的扩展数据逻辑分析方法

过程系统工程 | 更新时间：2026-07-24

- 面向工业混杂系统故障检测的扩展数据逻辑分析方法
- An extended logical analysis of data approach to fault detections of industrial hybrid systems
- 化工学报 2020年71卷第11期页码：5237-5245
- 作者机构：
  
  北京化工大学信息科学与技术学院，北京 100029
- 作者简介：
  
  孙中建（1996—），男，硕士研究生，sunzj@mail.buct.edu.cn
  李宏光（1963—），男，博士，教授，lihg@mail.buct.edu.cn
- 基金信息：
- DOI：10.11949/0438-1157.20200328
  中图分类号： TQ 028.8
- 收稿：2020-03-30，
  
  修回：2020-06-10，
  
  网络首发：2020-11-03，
  
  纸质出版：2020-11-05
- 稿件说明：
移动端阅览
孙中建, 杨博, 齐楚, 李宏光. 面向工业混杂系统故障检测的扩展数据逻辑分析方法[J]. 化工学报, 2020, 71(11): 5237-5245

SUN Zhongjian, YANG Bo, QI Chu, LI Hongguang. An extended logical analysis of data approach to fault detections of industrial hybrid systems[J]. CIESC Journal, 2020, 71(11): 5237-5245
孙中建, 杨博, 齐楚, 李宏光. 面向工业混杂系统故障检测的扩展数据逻辑分析方法[J]. 化工学报, 2020, 71(11): 5237-5245 DOI： 10.11949/0438-1157.20200328.

SUN Zhongjian, YANG Bo, QI Chu, LI Hongguang. An extended logical analysis of data approach to fault detections of industrial hybrid systems[J]. CIESC Journal, 2020, 71(11): 5237-5245 DOI： 10.11949/0438-1157.20200328.

摘要

常规的数据驱动故障检测方法难以处理同时包含连续和离散变量的工业混杂系统，数据逻辑分析（logical analysis of data， LAD）方法通过对历史数据中变量组合的逻辑分析，能够有效地挖掘离散和连续变量数据中存在的隐含规则。然而，常规的LAD在提取连续变量特征时存在对趋势变化信息丢失的问题，并且在处理具有高维度、多变量特征的工业数据时会导致提取的规则存在大量冗余。为此，本文提出一种基于扩展数据逻辑分析（extended logical analysis of data， ELAD）的工业混杂系统故障检测方法，根据与关键变量的关联度选取相关变量，增加变量的趋势信息以进行过程状态变化的表征，生成可解释的故障检测模型。应用于工业煤气化汽包过程，有效地检测了关键混杂变量对汽包液位故障的影响，实验结果验证了所提方法的可行性和有效性。

Abstract

It is difficult to deal with industrial hybrid systems involving both continuous and discrete variables using conventional data-driven fault detection methods. While logical analysis of data (LAD) methods are able to effectively explore hidden rules in discrete and continuous data by means of logical analysis for variable associations. However

conventional LAD has the problem of losing trend change information when extracting features of continuous variables. And when processing industrial data with high-dimensional

multivariate features

it will cause a lot of redundancy in the extracted rules. Motivated by these observations

this paper presents an extended logical analysis of data (ELAD) approach to fault detections of industrial hybrid systems. Therein

correlated variables are selected according to the association degree with key variables and additive variable trends are employed to characterize process status changes

creating an explicable fault detection model. The proposed method is applied to the steam drum process of an industrial coal gasification plant in detecting the influence of key hybrid variables on the fault of steam drum level. The results verify the feasibility and effectiveness of the contribution.

关键词

Keywords

references

Zhou G , Biswas G , Feng W . A comprehensive diagnosis of hybrid systems for discrete and parametric faults using hybrid I/O automata [J ] . Ifac Papersonline , 2015 , 48 ( 21 ): 143 - 149 .

Barton P I , Lee C K . Modeling, simulation, sensitivity analysis, and optimization of hybrid systems [J ] . ACM Transactions on Modeling and Computer Simulation , 2002 , 12 ( 4 ): 256 - 289 .

Maurya M R , Rengaswamy R , Venkatasubramanian V . A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis [J ] . Chemical Engineering Research & Design , 2007 , 85 ( 10 ): 1407 - 1422 .

李晗 , 萧德云 . 基于数据驱动的故障诊断方法综述 [J ] . 控制与决策 , 2011 , 26 ( 1 ): 1 - 9, 16 .

Li H , Xiao D Y . Survey on data driven fault diagnosis methods [J ] . Journal of Control and Decision , 2011 , 26 ( 1 ): 1 - 9, 16 .

Ming L , Zhao J . Review on chemical process fault detection and diagnosis [C ] // 6th International Symposium on Advanced Control of Industrial Processes , New Jersey : IEEE , 2017 : 457 - 462 .

Bezerra C G , Costa B S J , Guedes L A , et al . An evolving approach to unsupervised and Real-Time fault detection in industrial processes [J ] . Expert Systems with Applications , 2016 , 63 : 134 - 144 .

Sadeghi R , Hamidzadeh J . Automatic support vector data description [J ] . Soft Computing , 2018 , 22 ( 1 ): 147 - 158 .

Yu J , Yan X . Whole process monitoring based on unstable neuron output information in hidden layers of deep belief network [J ] . IEEE Transactions on Cybernetics , 2019 , PP( 99 ): 1 - 10 .

Heracleous C , Panayiotou C G , Polycarpou M M . Fault diagnosis of uncertain hybrid systems [J ] . IFAC-PapersOnLine , 2018 , 51 ( 24 ): 123 - 130 .

Lebbal M E H , Chafouk H , Hoblos G , et al . Faults detection and isolation for non linear hybrid systems [J ] . IFAC Proceedings Volumes , 2006 , 39 ( 13 ): 986 - 991 .

Krell M M , Straube S . Backtransformation: a new representation of data processing chains with a scalar decision function [J ] . Advances in Data Analysis and Classification , 2017 , 11 ( 2 ): 415 - 439 .

Ragab A , El-Koujok M , Amazouz M , et al . Fault detection and diagnosis in the Tennessee Eastman process using interpretable knowledge discovery [C ] // 2017 Annual Reliability and Maintainability Symposium , New Jersey : IEEE , 2017 : 1 - 7 .

Lopez-Soto D , Yacout S , Angel-Bello F . Root cause analysis of familiarity biases in classification of inventory items based on logical patterns recognition [J ] . Computers and Industrial Engineering , 2016 , 93 ( C ): 121 - 130 .

Kogan A , Lejeune M A . Combinatorial methods for constructing credit risk ratings [M ] // Lee C F, Lee J. Handbook of Financial Econometrics and Statistics . New York : Springer , 2015 : 439 - 483 .

Kronek L P , Reddy A A . Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data [J ] . Bioinformatics , 2008 , 24 ( 16 ): i248 - i253 .

Mortada M A , Yacout S , Lakis A . Fault diagnosis in power transformers using multi-class logical analysis of data [J ] . Journal of Intelligent Manufacturing , 2014 , 25 ( 6 ): 1429 - 1439 .

Shaban Y , Yacout S , Balazinski M , et al . Cutting tool wear detection using multiclass logical analysis of data [J ] . Machining Science and Technology , 2017 , 21 ( 4 ): 526 - 541 .

Jocelyn S , Chinniah Y , Ouali M S , et al . Application of logical analysis of data to machinery-related accident prevention based on scarce data [J ] . Reliability Engineering and System Safety , 2017 , 159 : 223 - 236 .

Ragab A , El-Koujok M , Poulin B , et al . Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data [J ] . Expert Systems with Applications , 2017 , 95 : 368 - 383 .

Jie Y , Yan X . Mutual information-dynamic stacked sparse autoencoders for fault detection [J ] . Industrial & Engineering Chemistry Research , 2019 , 58 ( 47 ): 21614 - 21624 .

Boros E , Crama Y , Hammer P L , et al . Logical analysis of data: classification with justification [J ] . Annals of Operations Research , 2011 , 188 ( 1 ): 33 - 61 .

Boros E , Hammer P L , Ibaraki T , et al . Logical analysis of numerical data [J ] . Mathematical Programming , 1997 , 79 ( 1 ): 163 - 190 .

Han J , Kim N , Yum B J , et al . Pattern selection approaches for the logical analysis of data considering the outliers and the coverage of a pattern [J ] . Expert Systems with Applications , 2011 , 38 ( 11 ): 13857 - 13862 .

Chikalov I , Lozin V , Lozina I , et al . Logical analysis of data: Theory, methodology and applications [M ] // Three Approaches to Data Analysis . Springer Berlin Heidelberg , 2013 : 147 - 192 .

Crama Y , Hammer P L , Ibaraki T . Cause-effect relationships and partially defined Boolean functions [J ] . Annals of Operations Research , 1988 , 16 ( 1 ): 299 - 325 .

Crama Y , Hammer P L . Boolean Functions: Theory, Algorithms, and Applications [M ] . New York : Cambridge University Press , 2011 : 511 - 526 .

Boros E , Hammer P L , Ibaraki T , et al . An implementation of logical analysis of data [J ] . IEEE Transactions on Knowledge and Data Engineering , 2000 , 12 ( 2 ): 292 - 306 .

Bonates T O , Hammer P L , Kogan A . Maximum patterns in datasets [J ] . Discrete Applied Mathematics , 2008 , 156 ( 6 ): 846 - 861 .

Velliangiri S , Alagumuthukrishnan S . A review of dimensionality reduction techniques for efficient computation [J ] . Procedia Computer Science , 2019 , 165 : 104 - 111 .

Singh T , Patnaik A , Chauhan R . Optimization of tribological properties of cement kiln dust-filled brake pad using grey relation analysis [J ] . Materials and Design , 2016 , 89 : 1335 - 1342 .

Li G D , Yamaguchi D , Nagai M . A grey-based decision-making approach to the supplier selection problem [J ] . Mathematical & Computer Modelling , 2007 , 46 ( 3/4 ): 573 - 581 .

Qiu B , Wang F , Li Y , et al . Research on method of simulation model validation based on improved grey relational analysis [J ] . Physics Procedia , 2012 , 25 : 1118 - 1125 .

Chan J W K , Tong T K L . Multi-criteria material selections and end-of-life product strategy: grey relational analysis approach [J ] . Materials and Design , 2007 , 28 ( 5 ): 1539 - 1546 .

李海林 , 郭崇慧 . 基于形态特征的时间序列符号聚合近似方法 [J ] . 模式识别与人工智能 , 2011 , 24 ( 5 ): 665 - 672 .

Li H L , Guo C H . Symbolic aggregate approximation based on shape features [J ] . Pattern Recognition and Artificial Intelligence , 2011 , 24 ( 5 ): 665 - 672 .

Chen N , Xue Y , Ding J , et al . Symbolizing for wind speed time series [J ] . Automation of Electric Power Systems , 2017 , 41 ( 11 ): 33 - 38 .

Lu B , Castillo I , Chiang L , et al . Industrial PLS model variable selection using moving window variable importance in projection [J ] . Chemometrics and Intelligent Laboratory Systems , 2014 , 135 : 90 - 109 .

Tang H , Liu S . Basic theory of fuzzy Bayesian networks and its application in machinery fault diagnosis [C ] // Fourth International Conference on Fuzzy Systems and Knowledge Discovery , New Jersey : IEEE Computer Society , 2007 : 132 - 137 .

Chelson P O . Reliability computation using fault tree analysis [R ] . Jet Propulsion Laboratory 32-1542 , 1971 .

Straube S , Krell M M . How to evaluate an agent's behavior to infrequent events?-Reliable performance estimation insensitive to class distribution [J ] . Frontiers in Computational Neuroscience , 2014 , 8 : 43 - 43 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于双子空间并行回归的化工过程质量相关故障检测方法

基于最大信息系数的分组支持向量数据描述故障检测

基于VAE-OCCA的质量相关故障检测方法研究

基于非线性动态模型的质子交换膜燃料电池故障诊断

基于核熵独立成分分析的故障检测方法