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PERFORMANCE OF PROGNOSIS INDICATORS FOR SUPERIMPOSED RENEWAL PROCESSES

Published online by Cambridge University Press:  01 June 2020

Xingheng Liu Open the ORCID record for Xingheng Liu [Opens in a new window] ,
Yann Dijoux Open the ORCID record for Yann Dijoux [Opens in a new window] ,
Jørn Vatn  and
Håkon Toftaker
Xingheng Liu
Affiliation:
The Laboratory of Systems Modelling and Dependability, Université de Technologie de Troyes, Troyes, France; Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway
Yann Dijoux
Affiliation:
The Laboratory of Systems Modelling and Dependability, Université de Technologie de Troyes, Troyes, France. E-mail: yann.dijoux@utt.fr
Jørn Vatn
Affiliation:
Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway
Håkon Toftaker
Affiliation:
Monitoring and Analysis, Bane NOR SF, Hamar, Norway
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Abstract

The paper deals with prognosis estimation for industrial systems in a series configuration, modeled by superimposed renewal processes (SRP), when the cause of failures is not available. In the presence of missing information, an SRP is commonly approximated by a Poisson process or a virtual age model. The performance of the approximations was assessed in the ideal configuration where all parameters of the models are known. The current article adopts a practitioner's perspective by assuming that the parameters of the models are unknown and must be estimated. In addition to inference procedures, the assessment of the prognosis indicators, such as the remaining useful life, is discussed. Finally, we investigate a fleet of infrastructure components of the Norwegian railway network operated by Bane NOR.

Keywords

applied probabilityreliability theorysimulationstochastic modelling
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 36 , Issue 1 , January 2022 , pp. 17 - 40
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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