List of peer-reviewed publications
- 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 36th AAAI Conference on Artificial Intelligence, 2022.
[BibTeX] [Download PDF]@inproceedings{DBLP:journals/corr/abs-2110-12662, 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 = {{36th AAAI Conference on Artificial Intelligence}}, year = {2022}, url = {https://arxiv.org/pdf/2110.12662.pdf} } - 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,” in 20th International Conference on Autonomous Agents and Multiagent Systems, 2021.
[BibTeX]@inproceedings{simao-et-al-aamas-2021, author = {Thiago D. Sim{\~{a}}o and Nils Jansen and Matthijs T. J. Spaan}, title = {AlwaysSafe: Reinforcement Learning without Safety Constraint Violations}, booktitle = {{20th International Conference on Autonomous Agents and Multiagent Systems}}, year = {2021}, note = {to appear}, keywords = {reviewed} } - 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} }
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} }