An overview on diagnosability and prognosability for system monitoring
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The complexification of systems has brought the emergence of a new field of study: system health monitoring. This field is deemed necessary because it improves system availability and it avoids unnecessary maintenance costs. System health monitoring is performed through diagnosis and prognosis methods. Diagnosis consists in detecting and identifying faults that may lead to system failures. Prognosis is related to the prediction of the system Remaining Useful Life (RUL) that corresponds to the remaining time until the system failure. This paper aims at giving an overview on the properties related to diagnosis and prognosis on different types of systems. We will focus on the diagnosability and prognosability properties. This paper will first briefly present the different types of systems of interest for the system health monitoring community. We will consider Discrete Event Systems (DES), Continuous Systems (CS), Hybrid Systems (HS) or Heterogeneous Systems (HtS). The rest of this paper will present the definitions given in the literature for the concepts of diagnosability and prognosability. The similarities and differences in these definitions for the different types of systems will be highlighted. Some metrics associated with the prognosability property will also be discussed.
How to Cite
##plugins.themes.bootstrap3.article.details##
System Health Monitoring Diagnosability Prognosability
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.