@article{Ton2023Sensors,

title = {Semantic Segmentation of Terrestrial Laser Scans of Railway Catenary Arches: A Use Case Perspective},

author = {Bram Ton and Faizan Ahmed and Jeroen Linssen},

url = {https://www.mdpi.com/1424-8220/23/1/222},

doi = {10.3390/s23010222},

issn = {1424-8220},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Sensors},

volume = {23},

number = {1},

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.},

keywords = {Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{10328827,

title = {Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review},

author = {Bram Dekker and Bram Ton and Joanneke Meijer and Nacir Bouali and Jeroen Linssen and Faizan Ahmed},

doi = {10.1109/ACCESS.2023.3337049},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE Access},

volume = {11},

pages = {134355-134373},

keywords = {Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{BukshJansenSaeed2021,

title = {Damage detection using in-domain and cross-domain transfer learning},

author = {Zaharah Allah Bukhsh and Nils Jansen and Aaqib Saeed},

url = {https://doi.org/10.1007/s00521-021-06279-x},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Neural Computing and Applications},

keywords = {Sensors},

pubstate = {published},

tppubtype = {article}

}

@article{app10238348,

title = {PrimaVera: Synergising Predictive Maintenance},

author = {Bram Ton and Rob Basten and John Bolte and Jan Braaksma and Alessandro Di Bucchianico and Philippe Calseyde and Frank Grooteman and Tom Heskes and Nils Jansen and Wouter Teeuw and Tiedo Tinga and Mariëlle Stoelinga},

url = {https://www.mdpi.com/2076-3417/10/23/8348},

doi = {10.3390/app10238348},

issn = {2076-3417},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Applied Sciences},

volume = {10},

number = {23},

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.},

keywords = {Organizational behavior, Prognostics, Sensors},

pubstate = {published},

tppubtype = {article}

}