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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Early fault detection based on wind turbine scada data using convolutional neural networks
Markus Ulmer, Eskil Jarlskog, Gianmarco Pizza, Jaakko Manninen, and Lilach Goren Huber. 2020. "Early fault detection based on wind turbine scada data using convolutional neural networks." PHM Society European Conference 5(1), 9, doi:10.36001/phme.2020.v5i1.1217
Cross-Turbine Training of Convolutional Neural Networks for SCADA-Based Fault Detection in Wind Turbines
Markus Ulmer, Eskil Jarlskog, Gianmarco Pizza, and Lilach Goren Huber. 2020. "Early fault detection based on wind turbine scada data using convolutional neural networks." Annual Conference of the PHM Society 12(1), 10, doi:10.36001/phmconf.2020.v12i1.1205
Transfer learning approaches for wind turbine fault detection using deep learning
Jannik Zgraggen, Markus Ulmer, Eskil Jarlskog, Gianmarco Pizza, and Lilach Goren Huber. 2021. "Early fault detection based on wind turbine scada data using convolutional neural networks." PHM Society European Conference 6(1), 12, doi:10.36001/phme.2021.v6i1.2835
Scaling Up Deep Learning Based Predictive Maintenance for Commercial Machine Fleets: a Case Study
Markus Ulmer, Jannik Zgraggen, Gianmarco Pizza, and Lilach Goren Huber. 2022. "Scaling up deep learning based predictive maintenance for commercial machine fleets: A case study." 9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland pp. 40-46, doi:10.1109/SDS54800.2022.00014
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data
Markus Ulmer, Jannik Zgraggen, and Lilach Goren Huber. 2024. "A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data." International Journal of Prognostics and Health Management 15(1), doi:10.36001/ijphm.2024.v15i1.3589
The impact of misclassifications and outliers on imputation methods
Matthias Templ and Markus Ulmer. 2024. "The impact of misclassifications and outliers on imputation methods." Journal of Applied Statistics 51(14), doi:10.1080/02664763.2024.2325969
Ancestor regression in structural vector autoregressive models
Christoph Schultheiss, Markus Ulmer, and Peter Bühlmann. 2024. "Ancestor regression in structural vector autoregressive models." arXiv e-prints, arXiv:2403.03778
Spectrally Deconfounded Random Forests
Markus Ulmer, Cyrill Scheidegger, and Peter Bühlmann. 2025. "Spectrally Deconfounded Random Forests." arXiv e-prints, arXiv:2502.03969