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Development and evaluation of a 2oo3 safety controller in FPGA using fault tree analysis and Markov models

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 14 No. 2 (2024)

DOI
10.11591/ijece.v14i2.pp1496-1507
ISSN
2722-2578, 2088-8708
Published
2024-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The Safety integrity level (SIL) is a measure of the reliability and availability of a safety instrumented system. SIL determination involves qualitative and quantitative analysis based on international standards such as IEC 61508 and IEC 61511. Several techniques can be used to analyze safety instrumented systems, including reliability block diagrams, fault tree analysis, and Markov models. The aim of this paper is to design and evaluate a pressure control system for a compressed nitrogen tank using a PID controller implemented in a field programmable gate array with 2 out of 3 architecture. This architecture ensures the safety of measurements and command of the system through a voting arrangement. The availability of the system is determined by the redundancy and the one hardware failure tolerance. The quantitative analysis is performed by calculating the probability of failure on demand per hour using Markov models or a relevant probabilistic approach based on fault tree analysis. The Markov model method gives the probability of failure of the system in different states during the system life cycle. The fault tree analysis method determines the probability of failure of the system using its equivalent failure rate. Furthermore, this paper compares the SIL result obtained by each model.

Collaboration network of the authors

3 authors · 3 co-authorship links · 2 across different organisations

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  • Abdelmalek Essaâdi University
  • Université Ibn Zohr
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Development and evaluation of a 2oo3 safety controller in FPGA using fault tree analysis and Markov models</dc:title><dc:creator>Nadir, Fatima Ezzahra</dc:creator><dc:creator>Bsiss, Mohammed</dc:creator><dc:creator>Amami, Benaissa</dc:creator><dc:description>&lt;jats:p&gt;The Safety integrity level (SIL) is a measure of the reliability and availability of a safety instrumented system. SIL determination involves qualitative and quantitative analysis based on international standards such as IEC 61508 and IEC 61511. Several techniques can be used to analyze safety instrumented systems, including reliability block diagrams, fault tree analysis, and Markov models. The aim of this paper is to design and evaluate a pressure control system for a compressed nitrogen tank using a PID controller implemented in a field programmable gate array with 2 out of 3 architecture. This architecture ensures the safety of measurements and command of the system through a voting arrangement. The availability of the system is determined by the redundancy and the one hardware failure tolerance. The quantitative analysis is performed by calculating the probability of failure on demand per hour using Markov models or a relevant probabilistic approach based on fault tree analysis. The Markov model method gives the probability of failure of the system in different states during the system life cycle. The fault tree analysis method determines the probability of failure of the system using its equivalent failure rate. Furthermore, this paper compares the SIL result obtained by each model.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2024-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v14i2.pp1496-1507</dc:identifier><dc:identifier>10.11591/ijece.v14i2.pp1496-1507</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 14 No 2 (2024); 1496</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/33781/17225</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>