Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Santiago RUIZ-ARENAS; Zoltn RUSK; Imre HORVTH; Ricardo MEJ-GUTIERREZ

doi:10.1631/FITEE.1700277

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Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Regular Papers | Updated：2022-05-19

- Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems
- 系统探索用于信息物理系统故障诊断的基于信号的指标
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 2, Pages: 152-175(2019)
- Affiliations：
  
  Faculty of Industrial Design Engineering, Delft University of Technology, Delft 2600AA, the Netherlands
  Grupo de Investigacin en Ingeniera de Diseo, Universidad EAFIT, Medelln 050048, Colombia
- Author bio：
  
  Santiago RUIZ-ARENAS, E-mail: s.ruizarenas@tudelft.nl
  [ "Zoltn RUSK, E-mail: z.rusak@tudelft.nl" ]
  [ "Imre HORVTH, E-mail: i.horvath@tudelft.nl" ]
  [ "Ricardo MEJ-GUTIERREZ, E-mail: rmejiag@eafit.edu.co" ]
- Funds：
- DOI：10.1631/FITEE.1700277
  CLC： TP277
- Received：24 April 2017，
  
  Revised：2019-;2-15，
  
  Published：2019-02
- Accepted：
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Santiago RUIZ-ARENAS, Zoltn RUSK, Imre HORVTH, et al. Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 152-175.
DOI：

Santiago RUIZ-ARENAS, Zoltn RUSK, Imre HORVTH, et al. Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 152-175. DOI： 10.1631/FITEE.1700277.

摘要

某些关键任务系统（MCS）的故障可能导致生命损失、环境破坏和（或）成本升高。因此，识别新故障和定期维护是保证MCS可靠运行的关键。依据实际生产过程中的指标来判断，是一种实用的故障识别和预测方法。基于监测系统特定运行模式下通用信号特征的统计变化，旨在开发一种主动故障诊断和预测方法。本文提出一种基于系统信号表现的故障识别新方法，利用了系统运行模式的动态管理，以及多源异构信号源的同步处理与表征。通过研究不同操作模式下的系统性能，提高故障诊断和预测的可靠性。通过正常与故障运行之间的信号特征统计偏差组成的故障指标矩阵，推断故障及其生成机理；提出"故障指标"概念，用作信息物理系统中即插即用的故障诊断与预测。实验结果表明该方法可以自动诊断MCS中的故障，为预防性维护决策支持系统的开发提供了支撑。

Abstract

Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life

damage the environments

and/or lead to high costs. Therefore

recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper

we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes

includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations

and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.

关键词

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references

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Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems

Related Author

Fei-Yue WANG

Jianbo GUO

Guangquan BU

Jun Jason ZHANG

Related Institution

The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences

State Grid Cooperation of China

China Electric Power Research Institute

School of Electrical Engineering and Automation, Wuhan University

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