Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Lisandro Arturo Jimenez Roa; Matthias Volk; Mariëlle I.A. Stoelinga

doi:10.1007/978-3-031-14835-4_4

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Lisandro Arturo Jimenez Roa^*, Matthias Volk, Mariëlle I.A. Stoelinga

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

108 Downloads (Pure)

Abstract

We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.

We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes.

Original language	English
Title of host publication	Computer Safety, Reliability, and Security
Subtitle of host publication	41st International Conference, SAFECOMP 2022, Munich, Germany, September 6–9, 2022, Proceedings
Editors	Mario Trapp, Francesca Saglietti, Marc Spisländer, Friedemann Bitsch
Publisher	Springer
Pages	46-61
Number of pages	16
ISBN (Electronic)	978-3-031-14835-4
ISBN (Print)	978-3-031-14834-7
DOIs	https://doi.org/10.1007/978-3-031-14835-4_4
Publication status	Published - 25 Aug 2022
Event	41st International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2022 - Munich, Germany Duration: 6 Sept 2022 → 9 Sept 2022 Conference number: 41

Publication series

Name	Lecture notes in computer science
Volume	13414

Conference

Conference	41st International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2022
Abbreviated title	SAFECOMP 2022
Country/Territory	Germany
City	Munich
Period	6/09/22 → 9/09/22

Keywords

22/3 OA procedure

Access to Document

10.1007/978-3-031-14835-4_4

10.1007_978-3-031-14835-4_4Final published version, 641 KBLicence: Taverne

Cite this

Jimenez Roa, L. A., Volk, M., & Stoelinga, M. I. A. (2022). Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization. In M. Trapp, F. Saglietti, M. Spisländer, & F. Bitsch (Eds.), Computer Safety, Reliability, and Security: 41st International Conference, SAFECOMP 2022, Munich, Germany, September 6–9, 2022, Proceedings (pp. 46-61). (Lecture notes in computer science; Vol. 13414). Springer. https://doi.org/10.1007/978-3-031-14835-4_4

Jimenez Roa, Lisandro Arturo ; Volk, Matthias ; Stoelinga, Mariëlle I.A. / Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization. Computer Safety, Reliability, and Security: 41st International Conference, SAFECOMP 2022, Munich, Germany, September 6–9, 2022, Proceedings. editor / Mario Trapp ; Francesca Saglietti ; Marc Spisländer ; Friedemann Bitsch. Springer, 2022. pp. 46-61 (Lecture notes in computer science).

@inproceedings{06b8b2d008404afaa1ddeb7c04ea8864,

title = "Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization",

abstract = "We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes.",

keywords = "22/3 OA procedure",

author = "{Jimenez Roa}, {Lisandro Arturo} and Matthias Volk and Stoelinga, {Mari{\"e}lle I.A.}",

note = "Funding Information: This research has been partially funded by NWO under the grant PrimaVera number NWA.1160.18.238 and by the ERC Consolidator grant CAESAR number 864075. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 41st International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2022, SAFECOMP 2022 ; Conference date: 06-09-2022 Through 09-09-2022",

year = "2022",

month = aug,

day = "25",

doi = "10.1007/978-3-031-14835-4_4",

language = "English",

isbn = "978-3-031-14834-7",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "46--61",

editor = "Mario Trapp and Francesca Saglietti and Marc Spisl{\"a}nder and Friedemann Bitsch",

booktitle = "Computer Safety, Reliability, and Security",

address = "Germany",

}

Jimenez Roa, LA, Volk, M & Stoelinga, MIA 2022, Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization. in M Trapp, F Saglietti, M Spisländer & F Bitsch (eds), Computer Safety, Reliability, and Security: 41st International Conference, SAFECOMP 2022, Munich, Germany, September 6–9, 2022, Proceedings. Lecture notes in computer science, vol. 13414, Springer, pp. 46-61, 41st International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2022, Munich, Germany, 6/09/22. https://doi.org/10.1007/978-3-031-14835-4_4

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization. / Jimenez Roa, Lisandro Arturo; Volk, Matthias; Stoelinga, Mariëlle I.A.
Computer Safety, Reliability, and Security: 41st International Conference, SAFECOMP 2022, Munich, Germany, September 6–9, 2022, Proceedings. ed. / Mario Trapp; Francesca Saglietti; Marc Spisländer; Friedemann Bitsch. Springer, 2022. p. 46-61 (Lecture notes in computer science; Vol. 13414).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

AU - Jimenez Roa, Lisandro Arturo

AU - Volk, Matthias

AU - Stoelinga, Mariëlle I.A.

N1 - Conference code: 41

PY - 2022/8/25

Y1 - 2022/8/25

N2 - We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes.

AB - We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes.

KW - 22/3 OA procedure

U2 - 10.1007/978-3-031-14835-4_4

DO - 10.1007/978-3-031-14835-4_4

M3 - Conference contribution

SN - 978-3-031-14834-7

T3 - Lecture notes in computer science

SP - 46

EP - 61

BT - Computer Safety, Reliability, and Security

A2 - Trapp, Mario

A2 - Saglietti, Francesca

A2 - Spisländer, Marc

A2 - Bitsch, Friedemann

PB - Springer

T2 - 41st International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2022

Y2 - 6 September 2022 through 9 September 2022

ER -

Jimenez Roa LA, Volk M, Stoelinga MIA. Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization. In Trapp M, Saglietti F, Spisländer M, Bitsch F, editors, Computer Safety, Reliability, and Security: 41st International Conference, SAFECOMP 2022, Munich, Germany, September 6–9, 2022, Proceedings. Springer. 2022. p. 46-61. (Lecture notes in computer science). doi: 10.1007/978-3-031-14835-4_4

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this