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
2023 |
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6. | Natália Ribeiro Marinho; Richard Loendersloot; Tiedo Tinga; Frank Grooteman; Jan Willem Wiegman: A Comparison of Optical Sensing Systems with Piezo-Electric Sensors for Impact Identification of Composite Plates. In: 14th International Workshop on Structural Health Monitoring, IWSHM 2023, pp. 1127–1133, DEStech Publications, Inc 2023. (Type: Proceedings Article | BibTeX | Tags: Sensors)@inproceedings{marinho2023comparison, |
5. | Bram Ton; Faizan Ahmed; Jeroen Linssen: Semantic Segmentation of Terrestrial Laser Scans of Railway Catenary Arches: A Use Case Perspective. In: Sensors, vol. 23, no. 1, 2023, ISSN: 1424-8220. (Type: Journal Article | Abstract | Links | BibTeX | Tags: Sensors)@article{Ton2023Sensors, 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. |
4. | Bram Dekker; Bram Ton; Joanneke Meijer; Nacir Bouali; Jeroen Linssen; Faizan Ahmed: Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review. In: IEEE Access, vol. 11, pp. 134355-134373, 2023. (Type: Journal Article | Links | BibTeX | Tags: Sensors)@article{10328827, |
2021 |
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3. | Nubia Nale Alves Silveira; Richard Loendersloot; Annemieke Angelique Meghoe; Tiedo 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, pp. 372–380, 2021, (6th European Conference of the Prognostics and Health Management Society, PHME 2021, PHME 2021 ; Conference date: 28-06-2021 Through 02-07-2021). (Type: Proceedings Article | Abstract | Links | BibTeX | Tags: Prognostics, Sensors)@inproceedings{daSilveria2021_PHM, 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. |
2. | Zaharah Allah Bukhsh; Nils Jansen; Aaqib Saeed: Damage detection using in-domain and cross-domain transfer learning. In: Neural Computing and Applications, 2021. (Type: Journal Article | Links | BibTeX | Tags: Sensors)@article{BukshJansenSaeed2021, |
2020 |
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1. | Bram Ton; Rob Basten; John Bolte; Jan Braaksma; Alessandro Di Bucchianico; Philippe Calseyde; Frank Grooteman; Tom Heskes; Nils Jansen; Wouter Teeuw; Tiedo Tinga; Mariëlle Stoelinga: PrimaVera: Synergising Predictive Maintenance. In: Applied Sciences, vol. 10, no. 23, 2020, ISSN: 2076-3417. (Type: Journal Article | Abstract | Links | BibTeX | Tags: Organizational behavior, Prognostics, Sensors)@article{app10238348, 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. |