Publications – Primavera Project

PrimaVera Demonstrators

Infrastructure Demonstrator – PrimaVera is developing a web app that showcases research applications in diagnostics, prognostics, and maintenance policy optimization and logistics. The work-in-progress web application can be visited via the link above.

List of scientific publications

Bayesian filters Decision tree Decision trees Decision-making under uncertainty Failure mechanisms Fault tree analysis Human decision-making Machine learning Maintenance optimization Model checking Model learning Organizational behavior Prognostics Reliability engineering Robustness Scenario optimization Sensors smart services Spare part management

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2021

Lisandro A. Jimenez-Roa; Tom Heskes; Mariëlle Stoelinga: Fault Trees, Decision Trees, and Binary Decision Diagrams: A systematic comparison. In: Castanier, Bruno; Cepin, Marko; Bigaud, David; Bérenguer, Christophe (Ed.): Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021), pp. 673–680, Research Publishing, 2021, (European Safety and Reliability Conference 2021, ESREL 2021 ; Conference date: 19-09-2021 Through 23-09-2021). (Type: Proceedings Article | Abstract | Links | BibTeX | Tags: Decision trees, Fault tree analysis, Reliability engineering)

@inproceedings{JimenezRoa2021_ESREL,

title = {Fault Trees, Decision Trees, and Binary Decision Diagrams: A systematic comparison},

author = {Lisandro A. Jimenez-Roa and Tom Heskes and Mariëlle Stoelinga},

editor = {Bruno Castanier and Marko Cepin and David Bigaud and Christophe Bérenguer},

url = {http://esrel2021.org/en/index.html},

doi = {10.3850/978-981-18-2016-8_241-cd},

year  = {2021},

date = {2021-09-21},

urldate = {2021-09-21},

booktitle = {Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)},

pages = {673–680},

publisher = {Research Publishing},

abstract = {In reliability engineering, we need to understand system dependencies, cause-effect relations, identify critical components, and analyze how they trigger failures. Three prominent graph models commonly used for these purposes are fault trees (FTs), decision trees (DTs), and binary decision diagrams (BDDs). These models are popular because they are easy to interpret, serve as a communication tool between stakeholders of various backgrounds, and support decision-making processes. Moreover, these models help to understand real-world problems by computing reliability metrics, minimum cut sets, logic rules, and displaying dependencies. Nevertheless, it is unclear how these graph models compare. Thus, the goal of this paper is to understand the similarities and differences through a systematic comparison based on their (i) purpose and application, (ii) structural representation, (iii) analysis methods, (iv) construction, and (v) benefits & limitations. Furthermore, we use a running example based on a Container Seal Design to showcase the models in practice. Our results show that, given that FTs, DTs, and BDDs have different purposes and application domains, they adopt different structural representations and analysis methodologies that entail a variety of benefits and limitations, the latter can be addressed via conversion methods or extensions. Specific remarks are that BDDs can be considered as a compact representation of binary DTs, since the former allows sub-node sharing, which makes BDDs more efficient at representing logical rules than binary DTs. It is possible to obtain cut sets from BDDs and DTs and construct a FT using the (con/dis)junctive normal form, although this may result in a sub-optimal FT structure.},

note = {European Safety and Reliability Conference 2021, ESREL 2021 ; Conference date: 19-09-2021 Through 23-09-2021},

keywords = {Decision trees, Fault tree analysis, Reliability engineering},

pubstate = {published},

tppubtype = {inproceedings}

}