An extended logical analysis of data approach to fault detections of industrial hybrid systems

SUN Zhongjian; YANG Bo; QI Chu; LI Hongguang

doi:10.11949/0438-1157.20200328

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An extended logical analysis of data approach to fault detections of industrial hybrid systems

Process system engineering | 更新时间：2026-07-24

- An extended logical analysis of data approach to fault detections of industrial hybrid systems
- CIESC Journal Vol. 71, Issue 11, Pages: 5237-5245(2020)
- 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：
- 基金信息：
- DOI：10.11949/0438-1157.20200328
  CLC： TQ 028.8
- Received：30 March 2020，
  
  Revised：2020-06-10，
  
  Online First：03 November 2020，
  
  Published：05 November 2020
- 稿件说明：
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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.

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references

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