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by Thom Badings

List of peer-reviewed publications

  • C. Koprulu, T. D. Simão, N. Jansen, and U. Topcu, “Risk-aware Curriculum Generation for Heavy-tailed Task Distributions,” in Uai, 2023.
    [BibTeX] 
    @inproceedings{Koprulu2023,
title = {{Risk-aware Curriculum Generation for Heavy-tailed Task Distributions}},
author = {
Cevahir Koprulu and
Thiago D. Sim{\~a}o and
Nils Jansen and
Ufuk Topcu
},
booktitle = {UAI},
year = {2023},
abbr = {UAI},
}

  • T. Badings, S. Junges, A. Marandi, U. Topcu, and N. Jansen, “Efficient sensitivity analysis for parametric robust markov chains,” in CAV, 2023.
    [BibTeX] 
    @inproceedings{BadingsCAV2023,
author = {Thom Badings and
Sebastian Junges and
Ahmadreza Marandi and
Ufuk Topcu and
Nils Jansen},
title = {Efficient Sensitivity Analysis for Parametric Robust Markov Chains},
booktitle = {{CAV}},
publisher = {Springer},
year = {2023},
abbr = {CAV},
bibtex_show = {true},
code = {https://github.com/LAVA-LAB/prmc-sensitivity},
}

  • P. Wienhöft, M. Suilen, T. D. Simão, C. Dubslaff, C. Baier, and N. Jansen, “More for less: safe policy improvement with stronger performance guarantees,” in IJCAI, 2023.
    [BibTeX] 
    @inproceedings{Wienhoft2023more,
author = {
Patrick Wienhöft and
Marnix Suilen and
Thiago D. Sim{\~{a}}o and
Clemens Dubslaff and
Christel Baier and
Nils Jansen
},
title = {More for Less: Safe Policy Improvement with Stronger Performance Guarantees},
booktitle = {{IJCAI}},
year = {2023},
abbr = {IJCAI},
}

  • W. Koops, N. Jansen, S. Junges, and T. D. Simão, “Recursive small-step multi-agent a* for dec-pomdps,” in IJCAI, 2023.
    [BibTeX] 
    @inproceedings{Koops2023recursive,
author = {
Wietze Koops and
Nils Jansen and
Sebastian Junges and
Thiago D. Sim{\~{a}}o},
title = {Recursive Small-Step Multi-Agent A* for Dec-POMDPs},
booktitle = {{IJCAI}},
year = {2023},
abbr = {IJCAI},
}

  • B. Ton, F. Ahmed, and J. Linssen, “Semantic segmentation of terrestrial laser scans of railway catenary arches: a use case perspective,” Sensors, vol. 23, iss. 1, 2023. doi:10.3390/s23010222
    [BibTeX] [Abstract] [Download PDF]

    Having access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.

    @Article{Ton2023sensors,
AUTHOR = {Ton, Bram and Ahmed, Faizan and Linssen, Jeroen},
TITLE = {Semantic Segmentation of Terrestrial Laser Scans of Railway Catenary Arches: A Use Case Perspective},
JOURNAL = {Sensors},
VOLUME = {23},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {222},
URL = {https://www.mdpi.com/1424-8220/23/1/222},
PubMedID = {36616820},
ISSN = {1424-8220},
ABSTRACT = {Having access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.},
DOI = {10.3390/s23010222}
}

  • M. Krale, T. D. Simão, and N. Jansen, “Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring,” in ICAPS, 2023.
    [BibTeX] 
    @inproceedings{Krale2023act,
title = {{Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring}},
author = {
Merlijn Krale and
Thiago D. Sim{\~a}o and
Nils Jansen
},
year = {2023},
booktitle = {{ICAPS}},
}

  • T. Badings, T. D. Simão, M. Suilen, and N. Jansen, “Decision-making under uncertainty: beyond probabilities. Challenges and Perspectives,” STTT, 2023.
    [BibTeX] 
    @article{Badings2023Decision,
title = {{Decision-making under uncertainty: beyond probabilities. Challenges and Perspectives}},
author = {
Thom Badings and
Thiago D. Sim{\~a}o and
Marnix Suilen and
Nils Jansen
},
year = {2023},
journal = {{STTT}},
}

  • Y. Hogewind, T. D. Simão, T. Kachman, and N. Jansen, “Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation,” in Iclr, 2023.
    [BibTeX] [Download PDF] 
    @inproceedings{Hogewind2023safe,
title = {{Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation}},
author = {
Hogewind, Yannick and
Thiago D. Sim{\~a}o and
Kachman, Tal and
Jansen, Nils
},
booktitle = {ICLR},
year = {2023},
url = {https://openreview.net/forum?id=b39dQt_uffW},
}

  • B. van Oudenhoven, P. V. de Calseyde, R. Basten, and E. Demerouti, “Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective,” International journal of production research, pp. 1-20, 2022. doi:10.1080/00207543.2022.2154403
    [BibTeX] [Download PDF] 
    @article{Oudenhoven2022,
author = {Bas van Oudenhoven and Philippe Van de Calseyde and Rob Basten and Evangelia Demerouti},
title = {Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective},
journal = {International Journal of Production Research},
volume = {0},
number = {0},
pages = {1-20},
year = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/00207543.2022.2154403},
URL = {https://doi.org/10.1080/00207543.2022.2154403},
eprint = {https://doi.org/10.1080/00207543.2022.2154403}
}

  • T. Badings, L. Romao, A. Abate, D. Parker, H. A. Poonawala, M. Stoelinga, and N. Jansen, “Robust control for dynamical systems with non-gaussian noise via formal abstractions,” Journal of artificial intelligence research (to appear), p. 1–29, 2023.
    [BibTeX] 
    @article{Badings2023JAIR,
title = {Robust Control for Dynamical Systems with Non-Gaussian Noise via Formal Abstractions},
author = {
Thom Badings and
Licio Romao and
Alessandro Abate and
David Parker and
Hasan A. Poonawala and
Marielle Stoelinga and
Nils Jansen
},
journal = {Journal of Artificial Intelligence Research (to appear)},
pages = {1--29},
year = {2023}
}

  • T. D. Simão, M. Suilen, and N. Jansen, “Safe Policy Improvement for POMDPs via Finite-State Controllers,” in Aaai, 2023.
    [BibTeX] [Download PDF] 
    @inproceedings{Simao2023safe,
title = {{Safe Policy Improvement for POMDPs via Finite-State Controllers}},
author = {
Thiago D. Sim{\~a}o and
Marnix Suilen and
Nils Jansen
},
booktitle = {AAAI},
year = {2023},
url = {https://arxiv.org/abs/2301.04939},
}

  • S. Carr, N. Jansen, S. Junges, and U. Topcu, “Safe reinforcement learning via shielding for pomdps,” in To be presented at AAAI 2023, 2023.
    [BibTeX] [Download PDF] 
    @inproceedings{DBLP:journals/corr/abs-2204-00755,
author = {Steven Carr and
Nils Jansen and
Sebastian Junges and
Ufuk Topcu},
title = {Safe Reinforcement Learning via Shielding for POMDPs},
booktitle = {{To be presented at AAAI 2023}},
year = {2023},
url = {https://arxiv.org/abs/2204.00755}
}

  • T. S. Badings, L. Romao, A. Abate, and N. Jansen, “Probabilities are not enough: formal controller synthesis for stochastic dynamical models with epistemic uncertainty,” in AAAI, 2023.
    [BibTeX] [Download PDF] 
    @inproceedings{DBLP:journals/corr/abs-2210-05989,
author = {Thom S. Badings and
Licio Romao and
Alessandro Abate and
Nils Jansen},
title = {Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic
Dynamical Models with Epistemic Uncertainty},
booktitle = {{AAAI}},
year = {2023},
url = {https://arxiv.org/abs/2210.05989}
}

  • Q. Yang, T. D. Simão, S. H. Tindemans, and M. T. J. Spaan, “Safety-constrained reinforcement learning with a distributional safety critic,” Machine learning, p. 1–29, 2022.
    [BibTeX] [Download PDF] 
    @article{Yang2022safety-constrained,
title = {Safety-constrained reinforcement learning with a distributional safety critic},
author = {
Qisong Yang and
Thiago D. Sim{\~a}o and
Simon H. Tindemans and
Matthijs T. J. Spaan
},
journal = {Machine Learning},
pages = {1--29},
year = {2022},
publisher = {Springer},
url = {https://link.springer.com/article/10.1007/s10994-022-06187-8}
}

  • M. Suilen, T. D. Simão, D. Parker, and N. Jansen, “Robust anytime learning of markov decision processes,” in NeurIPS, 2022.
    [BibTeX] 
    @inproceedings{DBLP:journals/corr/abs-2205-15827,
author = {Marnix Suilen and
Thiago D. Sim{\~{a}}o and
David Parker and
Nils Jansen},
title = {Robust Anytime Learning of Markov Decision Processes},
booktitle = {{NeurIPS}},
year = {2022}
}

  • L. A. Jimenez-Roa, T. Heskes, T. Tinga, and M. Stoelinga, “Automatic inference of fault tree models via multi-objective evolutionary algorithms,” Ieee transactions on dependable and secure computing, pp. 1-12, 2022. doi:10.1109/TDSC.2022.3203805
    [BibTeX] 
    @ARTICLE{9875105,
author={Jimenez-Roa, Lisandro A. and Heskes, Tom and Tinga, Tiedo and Stoelinga, Mariëlle},
journal={IEEE Transactions on Dependable and Secure Computing},
title={Automatic inference of fault tree models via multi-objective evolutionary algorithms},
year={2022},
volume={},
number={},
pages={1-12},
doi={10.1109/TDSC.2022.3203805}}

  • L. A. Jimenez-Roa, M. Volk, and M. Stoelinga, “Data-driven inference of fault tree models exploiting symmetry and modularization,” in International conference on computer safety, reliability, and security, 2022, p. 46–61.
    [BibTeX] 
    @inproceedings{jimenez2022data,
title={Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization},
author={Jimenez-Roa, Lisandro Arturo and Volk, Matthias and Stoelinga, Mari{\"e}lle},
booktitle={International Conference on Computer Safety, Reliability, and Security},
pages={46--61},
year={2022},
organization={Springer}
}

  • L. A. Jimenez-Roa, T. Heskes, T. Tinga, H. J. Molegraaf, and M. Stoelinga, “Deterioration modeling of sewer pipes via discrete-time markov chains: a large-scale case study in the netherlands,” in 32nd european safety and reliability conference, esrel 2022: understanding and managing risk and reliability for a sustainable future, 2022, p. 1299–1306.
    [BibTeX] 
    @inproceedings{jimenez2022deterioration,
title={Deterioration modeling of sewer pipes via discrete-time Markov chains: A large-scale case study in the Netherlands},
author={Jimenez-Roa, Lisandro A and Heskes, Tom and Tinga, Tiedo and Molegraaf, Hajo JA and Stoelinga, Mari{\"e}lle},
booktitle={32nd European Safety and Reliability Conference, ESREL 2022: Understanding and Managing Risk and Reliability for a Sustainable Future},
pages={1299--1306},
year={2022}
}

  • T. S. Badings, M. Cubuktepe, N. Jansen, S. Junges, J. -, and U. Topcu, “Scenario-based verification of uncertain parametric mdps,” Int. j. softw. tools technol. transf., vol. 24, iss. 5, p. 803–819, 2022.
    [BibTeX] [Download PDF] 
    @article{DBLP:journals/sttt/BadingsCJJKT22,
author = {Thom S. Badings and
Murat Cubuktepe and
Nils Jansen and
Sebastian Junges and
Joost{-}Pieter Katoen and
Ufuk Topcu},
title = {Scenario-based verification of uncertain parametric MDPs},
journal = {Int. J. Softw. Tools Technol. Transf.},
volume = {24},
number = {5},
pages = {803--819},
year = {2022},
url = {https://arxiv.org/abs/2112.13020}
}

  • T. S. Badings, N. Jansen, S. Junges, M. Stoelinga, and M. Volk, “Sampling-based verification of ctmcs with uncertain rates,” in CAV (2), 2022, p. 26–47.
    [BibTeX] [Download PDF] 
    @inproceedings{DBLP:conf/cav/BadingsJJSV22,
author = {Thom S. Badings and
Nils Jansen and
Sebastian Junges and
Mari{\"{e}}lle Stoelinga and
Matthias Volk},
title = {Sampling-Based Verification of CTMCs with Uncertain Rates},
booktitle = {{CAV} {(2)}},
series = {Lecture Notes in Computer Science},
volume = {13372},
pages = {26--47},
publisher = {Springer},
year = {2022},
url = {https://arxiv.org/abs/2205.08300}
}

  • D. Kerkkamp, Z. A. Bukhsh, Y. Zhang, and N. Jansen, “Grouping of maintenance actions with deep reinforcement learning and graph convolutional networks,” in International Conference on Agents and Artificial Intelligence (ICAART), 2022.
    [BibTeX] 
    @inproceedings{Kerkkamp2021,
author = {David Kerkkamp and
Zaharah A. Bukhsh and
Yingqian Zhang and
Nils Jansen},
title = {Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks},
booktitle = {{International Conference on Agents and Artificial Intelligence (ICAART)}},
year = {2022}
}

  • T. S. Badings, A. Abate, N. Jansen, D. Parker, H. A. Poonawala, and M. Stoelinga, “Sampling-based robust control of autonomous systems with non-gaussian noise,” in AAAI, 2022, p. 9669–9678.
    [BibTeX] [Download PDF] 
    @inproceedings{DBLP:conf/aaai/BadingsA00PS22,
author = {Thom S. Badings and
Alessandro Abate and
Nils Jansen and
David Parker and
Hasan A. Poonawala and
Mari{\"{e}}lle Stoelinga},
title = {Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian
Noise},
booktitle = {{AAAI}},
pages = {9669--9678},
publisher = {{AAAI} Press},
year = {2022},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/21201/20950}
}

  • L. S. Keizers, R. Loendersloot, and T. Tinga, “Unscented kalman filtering for prognostics under varying operational and environmental conditions,” International journal of prognostics and health management, vol. 12, iss. 2, 2021.
    [BibTeX] [Download PDF] 
    @article{keizers2021unscented,
title={Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions},
author={Keizers, Luc S. and Loendersloot, Richard and Tinga, Tiedo},
journal={International Journal of Prognostics and Health Management},
volume={12},
number={2},
year={2021},
url={http://www.papers.phmsociety.org/index.php/ijphm/article/view/2943}
}

  • Z. A. Bukhsh, N. Jansen, and A. Saeed, “Damage detection using in-domain and cross-domain transfer learning,” Neural computing and applications, 2021.
    [BibTeX] [Download PDF] 
    @article{BukshJansenSaeed2021,
author = {Zaharah Allah Bukhsh and
Nils Jansen and
Aaqib Saeed},
title = {Damage detection using in-domain and cross-domain transfer learning},
journal = {Neural Computing and Applications},
year = {2021},
url = {https://doi.org/10.1007/s00521-021-06279-x}
}

  • T. S. Badings, A. Hartmanns, N. Jansen, and M. Suilen, “Balancing wind and batteries: towards predictive verification of smart grids,” in 13th NASA Formal Methods Symposium, 2021.
    [BibTeX] [Download PDF] 
    @inproceedings{Badings2021NFM,
author = {Thom S. Badings and
Arnd Hartmanns and
Nils Jansen and
Marnix Suilen},
title = {Balancing Wind and Batteries: Towards Predictive Verification of Smart
Grids},
booktitle = {{13th NASA Formal Methods Symposium}},
year = {2021},
url={https://arxiv.org/abs/2101.12496}
}

  • T. D. Simão, N. Jansen, and M. T. J. Spaan, “AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training,” in Proceedings of the 20th international conference on autonomous agents and multiagent systems (aamas), 2021, pp. 1226-1235.
    [BibTeX] [Download PDF] 
    @inproceedings{Simao2021alwayssafe,
author = {
Thiago D. Sim{\~a}o and
Nils Jansen and
Matthijs T. J. Spaan
},
title = {{AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training}},
year = {2021},
booktitle = {Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS)},
publisher = {IFAAMAS},
pages = {1226-1235},
url = {https://ifaamas.org/Proceedings/aamas2021/pdfs/p1226.pdf}
}

  • M. Cubuktepe, N. Jansen, S. Junges, A. Marandi, M. Suilen, and U. Topcu, “Robust finite-state controllers for uncertain pomdps,” in 35th AAAI Conference on Artificial Intelligence, 2021.
    [BibTeX] 
    @inproceedings{cubuktepe-et-al-aaai-2021,
author = {Murat Cubuktepe and
Nils Jansen and
Sebastian Junges and
Ahmadreza Marandi and
Marnix Suilen and
Ufuk Topcu},
title = {Robust Finite-State Controllers for Uncertain POMDPs},
booktitle  = {{35th AAAI Conference on Artificial Intelligence}},
year = {2021},
keywords = {reviewed}
}

  • B. Ton, R. Basten, J. Bolte, J. Braaksma, A. Di Bucchianico, P. van de Calseyde, F. Grooteman, T. Heskes, N. Jansen, W. Teeuw, T. Tinga, and M. Stoelinga, “Primavera: synergising predictive maintenance,” Applied sciences, vol. 10, iss. 23, 2020. doi:10.3390/app10238348
    [BibTeX] [Abstract] [Download PDF]

    The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

    @Article{app10238348,
AUTHOR = {Ton, Bram and Basten, Rob and Bolte, John and Braaksma, Jan and Di Bucchianico, Alessandro and van de Calseyde, Philippe and Grooteman, Frank and Heskes, Tom and Jansen, Nils and Teeuw, Wouter and Tinga, Tiedo and Stoelinga, Mariëlle},
TITLE = {PrimaVera: Synergising Predictive Maintenance},
JOURNAL = {Applied Sciences},
VOLUME = {10},
YEAR = {2020},
NUMBER = {23},
ARTICLE-NUMBER = {8348},
URL = {https://www.mdpi.com/2076-3417/10/23/8348},
ISSN = {2076-3417},
ABSTRACT = {The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.},
DOI = {10.3390/app10238348}
}

  • L. A. Jimenez-Roa, T. Heskes, and M. Stoelinga, “Fault trees, decision trees, and binary decision diagrams: a systematic comparison,” in Proceedings of the 31st european safety and reliability conference (esrel 2021), 2021, p. 673–680. doi:10.3850/978-981-18-2016-8_241-cd
    [BibTeX] [Abstract] [Download PDF]

    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.

    @inproceedings{JimenezRoa2021_ESREL,
title = "Fault Trees, Decision Trees, and Binary Decision Diagrams: A systematic comparison",
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.",
keywords = "Fault tree analysis, Decision tree, Binary decision diagrams, Systematic comparison, Reliability engineering, Decision making, Graph models",
author = "Lisandro A. Jimenez-Roa and Tom Heskes and Mari{\"e}lle Stoelinga",
year = "2021",
month = sep,
day = "21",
doi = "10.3850/978-981-18-2016-8_241-cd",
language = "English",
pages = "673--680",
editor = "Bruno Castanier and Marko Cepin and David Bigaud and Christophe B{\'e}renguer",
booktitle = "Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)",
publisher = "Research Publishing",
note = "European Safety and Reliability Conference 2021, ESREL 2021 ; Conference date: 19-09-2021 Through 23-09-2021",
url = "http://esrel2021.org/en/index.html",
}

  • N. N. A. da Silveira, R. Loendersloot, A. A. Meghoe, and T. Tinga, “Data selection criteria for the application of predictive maintenance to centrifugal pumps,” in Proceedings of the 6th european conference of the prognostics and health management societ, 2021, p. 372–380.
    [BibTeX] [Abstract] [Download PDF]

    The maintenance of vehicles and components is present in most people’s daily lives, ranging from changing a private vehicle’s oil to the failure prediction of an aircraft component during flight. Usually, the manufacturer’s maintenance recommendation is a good solution when the cost is not too high, and the real application is used as indicated by the manufacturer. However, this recommendation can turn unfeasible when there is a significant variation in operational conditions or high maintenance costs. In these cases, the manufacturer’s suggestion is typically conservative, leading to unnecessarily high costs. Therefore, the challenge is to find the best approach for optimizing a component’s maintenance, given the system in which it is integrated and the associated operational and environmental conditions. Nevertheless, the available information on the loads on the component also plays a role in that choice. This paper proposes to combine case-specific information with generic degradation prediction models to obtain an acceptable but also affordable approach. The objective is to develop data selection criteria to indicate the parameters that have a high impact on the failure prediction, in this case, of a generic impeller pump. Subsequently, the approach delivers to the user an indication of the component remaining useful life using different operational scenarios.

    @inproceedings{daSilveria2021_PHM,
title = "Data Selection Criteria for the Application of Predictive Maintenance to Centrifugal Pumps",
abstract = "The maintenance of vehicles and components is present in most people's daily lives, ranging from changing a private vehicle's oil to the failure prediction of an aircraft component during flight. Usually, the manufacturer's maintenance recommendation is a good solution when the cost is not too high, and the real application is used as indicated by the manufacturer. However, this recommendation can turn unfeasible when there is a significant variation in operational conditions or high maintenance costs. In these cases, the manufacturer's suggestion is typically conservative, leading to unnecessarily high costs. Therefore, the challenge is to find the best approach for optimizing a component's maintenance, given the system in which it is integrated and the associated operational and environmental conditions. Nevertheless, the available information on the loads on the component also plays a role in that choice. This paper proposes to combine case-specific information with generic degradation prediction models to obtain an acceptable but also affordable approach. The objective is to develop data selection criteria to indicate the parameters that have a high impact on the failure prediction, in this case, of a generic impeller pump. Subsequently, the approach delivers to the user an indication of the component remaining useful life using different operational scenarios. ",
author = "Nubia Nale Alves da Silveira and Richard Loendersloot and Meghoe, Annemieke Angelique and Tiedo Tinga",
year = "2021",
month = jun,
language = "English",
series = "Archives of the PHM Society European Conference",
number = "1",
pages = "372--380",
booktitle = "Proceedings of the 6th European Conference of the Prognostics and Health Management Societ",
note = "6th European Conference of the Prognostics and Health Management Society, PHME 2021, PHME 2021 ; Conference date: 28-06-2021 Through 02-07-2021",
url = "https://phm-europe.org/",
}

List of student theses and internship reports

  • M. Crooymans, “Investigating the application of condition-based maintenance at the rnln using partially observable markov decision processes,” Eindhoven University of Technology. Internship performed at the Royal Netherlands Navy, 2020.
    [BibTeX] [Download PDF] 
    @Article{Crooymans2020,
AUTHOR = {Crooymans, Maud},
TITLE = {Investigating the Application of Condition-Based Maintenance at the RNLN using Partially Observable Markov Decision Processes},
JOURNAL = {{Eindhoven University of Technology. Internship performed at the Royal Netherlands Navy}},
YEAR = {2020},
URL = {https://primavera-project.com/wp-content/uploads/2020/12/Crooymans2020_1MSE15_RNLN.pdf}
}