Probabilistic Algorithm for System Level Self-diagnosis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F24%3A43898912" target="_blank" >RIV/44555601:13440/24:43898912 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-70959-3_11" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-70959-3_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-70959-3_11" target="_blank" >10.1007/978-3-031-70959-3_11</a>

Result language

angličtina

Original language name

Probabilistic Algorithm for System Level Self-diagnosis

Original language description

System-level self-diagnosis is one of the most important tasks in the field of computer science. In this paper, we present the results of the research on how to increase the credibility of the results of system diagnosis by way of merging two methods of system-level self-diagnostic (traditional and unconventional). As distinct from traditional system level self-diagnosis, unconventional method of system diagnosis can deal with arbitrary testing assignments and can be applied to heterogeneous systems. The diagnosis problem consists of determining the location of faults in the system (i.e., determining faulty units). In traditional system level self-diagnosis, such diagnosis problem can be defined as finding the necessary and sufficient conditions for a system testing assignment that should be satisfied to achieve a given level of diagnosability given a fault model and an allowable family of fault sets. For solving the diagnosis problem the appropriate diagnosis algorithms should be developed. Before designing a diagnosis algorithm it is needed to adopt the strategy that is suitable for the particular complex system. Among the possible diagnosis strategies, such as unique, sequential, excess and probabilistic, the probabilistic strategy was chosen. Based on this strategy, the diagnosis algorithms were designed. The results (credibility) of the algorithms that follow the probabilistic diagnosis strategy can be improved. For this purpose, some elements of unconventional system level self-diagnosis are used in the algorithm design. Short description of unconventional system level self-diagnosis is presented in this paper. The obtained results of improved diagnosis allow revealing the functional dependence of the credibility of diagnosis results on the values of system and testing parameters.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Data Engineering, Computational Intelligence, and Decision-Making, Volume 1

ISBN

978-3-031-70958-6

ISSN

2367-4512

e-ISSN

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Number of pages

23

Pages from-to

219-241

Publisher name

Springer

Place of publication

Berlín

Event location

Ústí nad Labem

Event date

Jun 19, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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