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CLC number: TP277

On-line Access: 2019-03-11

Received: 2017-04-24

Revision Accepted: 2017-12-16

Crosschecked: 2019-02-15

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Santiago Ruzi-arenas

https://orcid.org/0000-0002-4018-7370

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.2 P.152-175

10.1631/FITEE.1700277


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


Author(s):  Santiago Ruzi-arenas, Zoltán Rusák, Imre Horváth, Ricardo Mejí-gutierrez

Affiliation(s):  Faculty of Industrial Design Engineering, Delft University of Technology, Delft 2600AA, the Netherlands; more

Corresponding email(s):   s.ruizarenas@tudelft.nl, z.rusak@tudelft.nl, i.horvath@tudelft.nl, rmejiag@eafit.edu.con

Key Words:  Failure indicators, Failure classification, Failure detection and diagnosis, Complex systems


Santiago Ruzi-arenas, Zoltán Rusák, Imre Horváth, Ricardo Mejí-gutierrez. 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.

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doi="10.1631/FITEE.1700277"
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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.

系统探索用于信息物理系统故障诊断的基于信号的指标

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。

关键词:故障指标;故障分类;故障检测和诊断;复杂系统

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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