133 citations · 433 across the 9 of their papers we have counts for
9 papers
k-space Physics-informed Neural Network (k-PINN) for Compressed Spectral Mapping and Efficient Inversion of Vibrations in Thin Composite Laminates
Saeid Hedayatrasa, Olga Fink, Wim Van Paepegem +1
The vibrational response of structural components carries valuable information about their underlying mechanical properties, health status and operational conditions. This undersco…
DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems
Mengjie Zhao, Olga Fink
In the Industrial Internet of Things (IIoT), condition monitoring sensor signals from complex systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying…
Collective Relational Inference for learning heterogeneous interactions
Zhichao Han, Olga Fink, David S. Kammer
Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. These interacting systems can be mo…
Filter-informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis
Tianfu Li, Chuang Sun, Olga Fink +3
Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also been i…
Incentive Mechanism in the Sponsored Content Market with Network Effect
Mina Montazeri, Pegah Rokhforoz, Hamed Kebriaei +1
We propose an incentive mechanism for the sponsored content provider market in which the communication of users can be represented by a graph and the private information of the use…
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…
Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
Qin Wang, Cees Taal, Olga Fink
Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy…
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series
Gabriel Michau, Gaetan Frusque, Olga Fink
High-Frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep learning tools are designed for inputs of fix…
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
Qin Wang, Gabriel Michau, Olga Fink
Domain adaptation aims at improving model performance by leveraging the learned knowledge in the source domain and transferring it to the target domain. Recently, domain adversaria…