A complex process fault prognosis approach based on multivariate delayed sequences

XU Yuan; LIU Ying; ZHU Qunxiong

doi:10.3969/j.issn.0438-1157.2013.12.003

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A complex process fault prognosis approach based on multivariate delayed sequences

更新时间：2025-12-10

- A complex process fault prognosis approach based on multivariate delayed sequences
- CIESC Journal Issue 12, Pages: 4290-4295(2013)
- 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：10.3969/j.issn.0438-1157.2013.12.003
  CLC： P29
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
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XU Yuan, LIU Ying, ZHU Qunxiong. A complex process fault prognosis approach based on multivariate delayed sequences[J]. CIESC Journal, 2013, (12): 4290-4295.
DOI：

XU Yuan, LIU Ying, ZHU Qunxiong. A complex process fault prognosis approach based on multivariate delayed sequences[J]. CIESC Journal, 2013, (12): 4290-4295. DOI： 10.3969/j.issn.0438-1157.2013.12.003.

摘要

复杂过程故障预测是保证过程安全可靠运行的关键，而复杂系统的工作状态往往由多元时滞序列决定，该序列含有变量间的时滞信息及相关关系，具有一定的信息完备性。因此文章提出一种基于多元时滞序列驱动的复杂过程故障预测方法，该方法首先构建复杂系统的时滞符号有向图（TD-SDG）进而得到多元时滞序列，然后针对复杂系统变量多、关系复杂的问题，提出一种独立成分分析（ICA）和ELM神经网络集成的方法，此方法可快速获取多元时滞序列的独立成分从而建立监控统计量，最终达到故障预测的目的。通过在Tennessee Eastman（TE）过程上的仿真实验验证，表明所提方法能够至少提前15 min预测到故障，方便工作人员及时有效地采取措施。

Abstract

Complex process fault prognosis is a key scientific issue that ensures the security of the process and reliable operation

however complex systems work state is often determined by multivariate delayed sequence.It contains the relationship between the variables and the time delay information

so it has information completeness.So a complex process fault prognosis approach based on multivariate delayed sequences is proposed.First this method construct the Time Delay Signed Direct Digraph (TD-SDG) to get multi-delayed sequence

then combine Independent Component Analysis and ELM neural network to get the independent component of the multi-delayed sequence

and finally realize the purpose of fault prognosis of complex system.The simulation results on Tennessee Eastman process illustrate that the proposed method can predict fault earlier 15 min

increase operator's reaction time and detect the fault.

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