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

Santiago Ruiz Arenas, Zoltan Rusak, Imre Horvath, Ricardo Mejí-Gutierrez

Research output: Contribution to journalArticleScientificpeer-review


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.
Original languageEnglish
Pages (from-to)152-175
Number of pages24
JournalFrontiers of Information Technology and Electronic Engineering
Issue number2
Publication statusPublished - 2019


Dive into the research topics of 'Systematic exploration of signal-based inidicators for failure diagnosis in the context of cyber-physical systems'. Together they form a unique fingerprint.
  • Concept of a Voice-Enabled Digital Assistant for Predictive Maintenance in Manufacturing

    Wellsandt, S., Rusak, Z., Ruiz Arenas, S., Aschenbrenner, D., Hibernik, K. & Thoben, K. D., 2020, TESConf 2020 - 9th International Conference on Through-life Engineering Services. p. 1 8 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Cite this