106 citations · 111 across the 5 of their papers we have counts for
6 papers · 1 filter
Interpretable Prognostics with Concept Bottleneck Models
Florent Forest, Katharina Rombach, Olga Fink
Deep learning approaches have recently been extensively explored for the prognostics of industrial assets. However, they still suffer from a lack of interpretability, which hinders…
Learning Informative Health Indicators Through Unsupervised Contrastive Learning
Katharina Rombach, Gabriel Michau, Wilfried Bürzle +2
Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health stat…
Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation
Katharina Rombach, Gabriel Michau, Olga Fink
New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. W…
Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data
Katharina Rombach, Gabriel Michau, Olga Fink
Mislabeled samples are ubiquitous in real-world datasets as rule-based or expert labeling is usually based on incorrect assumptions or subject to biased opinions. Neural networks c…
Automated Machine Learning in Practice: State of the Art and Recent Results
Lukas Tuggener, Mohammadreza Amirian, Katharina Rombach +4
A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and m…
Deep Learning in the Wild
Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci +8
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range…