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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


Santiago Ruzi-arenas


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


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

Author(s):  Santiago Ruzi-arenas, Zoltn Rusk, Imre Horvth, Ricardo Mej-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, Zoltn Rusk, Imre Horvth, Ricardo Mej-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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publisher="Zhejiang University Press & Springer",

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T1 - Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems
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A1 - Imre Horvth
A1 - Ricardo Mej-gutierrez
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
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EP - 175
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700277

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.




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


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