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Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems. / Ruiz-Arenas, Santiago; Rusák, Zoltán; Horváth, Imre; Mejí-Gutierrez, Ricardo.

In: Frontiers of Information Technology and Electronic Engineering, Vol. 20, No. 2, 2019, p. 152-175.

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Ruiz-Arenas, Santiago ; Rusák, Zoltán ; Horváth, Imre ; Mejí-Gutierrez, Ricardo. / Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems. In: Frontiers of Information Technology and Electronic Engineering. 2019 ; Vol. 20, No. 2. pp. 152-175.

BibTeX

@article{09b056bd81be40969b8fae44d88bf3b6,
title = "Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems",
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.",
keywords = "Complex systems, Failure classification, Failure detection and diagnosis, Failure indicators, TP277",
author = "Santiago Ruiz-Arenas and Zolt{\'a}n Rus{\'a}k and Imre Horv{\'a}th and Ricardo Mej{\'i}-Gutierrez",
year = "2019",
doi = "10.1631/FITEE.1700277",
language = "English",
volume = "20",
pages = "152--175",
journal = "Frontiers of Information Technology and Electronic Engineering",
issn = "2095-9184",
publisher = "Zheijiang University Press",
number = "2",

}

RIS

TY - JOUR

T1 - Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

AU - Ruiz-Arenas, Santiago

AU - Rusák, Zoltán

AU - Horváth, Imre

AU - Mejí-Gutierrez, Ricardo

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Complex systems

KW - Failure classification

KW - Failure detection and diagnosis

KW - Failure indicators

KW - TP277

UR - http://www.scopus.com/inward/record.url?scp=85063196562&partnerID=8YFLogxK

U2 - 10.1631/FITEE.1700277

DO - 10.1631/FITEE.1700277

M3 - Article

VL - 20

SP - 152

EP - 175

JO - Frontiers of Information Technology and Electronic Engineering

T2 - Frontiers of Information Technology and Electronic Engineering

JF - Frontiers of Information Technology and Electronic Engineering

SN - 2095-9184

IS - 2

ER -

ID: 52737265