Knowledge-based diagnosis of process systems using procedure HAZID information

Ágnes Werner-Stark, Erzsébet Németh, K. Hangos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Earlier investigations show that the results of hazard identification (HAZID) and analysis (e.g. HAZOP or FMEA) can effectively be used for knowledge-based diagnosis of complex process systems in their steady-state operation. In order to extend this approach for transient operating conditions controlled by operating procedures, the notion of nominal input-output event sequences of qualitative signals has been introduced, and the deviations used in the procedure HAZID analysis have been defined therefrom. The diagnosis can then be performed algorithmically by matching the deviation sequences of the observed input-output event sequences and the nominal ones generated by qualitative dynamic models. The concepts and methods are illustrated using a simple case study consisting of a simple tank, controlled by an operating procedure.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages385-394
Number of pages10
Volume6883 LNAI
EditionPART 3
DOIs
Publication statusPublished - 2011
Event15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011 - Kaiserslautern, Germany
Duration: Sep 12 2011Sep 14 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6883 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011
CountryGermany
CityKaiserslautern
Period9/12/119/14/11

Fingerprint

Knowledge-based
Hazard
Hazards
Categorical or nominal
Deviation
Failure Modes and Effects Analysis
Dynamic models
Output
Dynamic Model

Keywords

  • discrete events
  • hazard identification
  • operating procedures
  • Process systems
  • qualitative model

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Werner-Stark, Á., Németh, E., & Hangos, K. (2011). Knowledge-based diagnosis of process systems using procedure HAZID information. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 6883 LNAI, pp. 385-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6883 LNAI, No. PART 3). https://doi.org/10.1007/978-3-642-23854-3_41

Knowledge-based diagnosis of process systems using procedure HAZID information. / Werner-Stark, Ágnes; Németh, Erzsébet; Hangos, K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6883 LNAI PART 3. ed. 2011. p. 385-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6883 LNAI, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Werner-Stark, Á, Németh, E & Hangos, K 2011, Knowledge-based diagnosis of process systems using procedure HAZID information. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 6883 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6883 LNAI, pp. 385-394, 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011, Kaiserslautern, Germany, 9/12/11. https://doi.org/10.1007/978-3-642-23854-3_41
Werner-Stark Á, Németh E, Hangos K. Knowledge-based diagnosis of process systems using procedure HAZID information. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 6883 LNAI. 2011. p. 385-394. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-23854-3_41
Werner-Stark, Ágnes ; Németh, Erzsébet ; Hangos, K. / Knowledge-based diagnosis of process systems using procedure HAZID information. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6883 LNAI PART 3. ed. 2011. pp. 385-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{eaeaf4f1d7a24f5b94df956da3f51772,
title = "Knowledge-based diagnosis of process systems using procedure HAZID information",
abstract = "Earlier investigations show that the results of hazard identification (HAZID) and analysis (e.g. HAZOP or FMEA) can effectively be used for knowledge-based diagnosis of complex process systems in their steady-state operation. In order to extend this approach for transient operating conditions controlled by operating procedures, the notion of nominal input-output event sequences of qualitative signals has been introduced, and the deviations used in the procedure HAZID analysis have been defined therefrom. The diagnosis can then be performed algorithmically by matching the deviation sequences of the observed input-output event sequences and the nominal ones generated by qualitative dynamic models. The concepts and methods are illustrated using a simple case study consisting of a simple tank, controlled by an operating procedure.",
keywords = "discrete events, hazard identification, operating procedures, Process systems, qualitative model",
author = "{\'A}gnes Werner-Stark and Erzs{\'e}bet N{\'e}meth and K. Hangos",
year = "2011",
doi = "10.1007/978-3-642-23854-3_41",
language = "English",
isbn = "9783642238536",
volume = "6883 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "385--394",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 3",

}

TY - GEN

T1 - Knowledge-based diagnosis of process systems using procedure HAZID information

AU - Werner-Stark, Ágnes

AU - Németh, Erzsébet

AU - Hangos, K.

PY - 2011

Y1 - 2011

N2 - Earlier investigations show that the results of hazard identification (HAZID) and analysis (e.g. HAZOP or FMEA) can effectively be used for knowledge-based diagnosis of complex process systems in their steady-state operation. In order to extend this approach for transient operating conditions controlled by operating procedures, the notion of nominal input-output event sequences of qualitative signals has been introduced, and the deviations used in the procedure HAZID analysis have been defined therefrom. The diagnosis can then be performed algorithmically by matching the deviation sequences of the observed input-output event sequences and the nominal ones generated by qualitative dynamic models. The concepts and methods are illustrated using a simple case study consisting of a simple tank, controlled by an operating procedure.

AB - Earlier investigations show that the results of hazard identification (HAZID) and analysis (e.g. HAZOP or FMEA) can effectively be used for knowledge-based diagnosis of complex process systems in their steady-state operation. In order to extend this approach for transient operating conditions controlled by operating procedures, the notion of nominal input-output event sequences of qualitative signals has been introduced, and the deviations used in the procedure HAZID analysis have been defined therefrom. The diagnosis can then be performed algorithmically by matching the deviation sequences of the observed input-output event sequences and the nominal ones generated by qualitative dynamic models. The concepts and methods are illustrated using a simple case study consisting of a simple tank, controlled by an operating procedure.

KW - discrete events

KW - hazard identification

KW - operating procedures

KW - Process systems

KW - qualitative model

UR - http://www.scopus.com/inward/record.url?scp=80053135514&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053135514&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-23854-3_41

DO - 10.1007/978-3-642-23854-3_41

M3 - Conference contribution

SN - 9783642238536

VL - 6883 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 385

EP - 394

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -