102 citations · 112 across the 6 of their papers we have counts for
7 papers
Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems
Vinay Sharma, Rémi Tanguy Oddon, Pietro Tesini +3
Accurate real-time modeling of multi-body dynamical systems is essential for enabling digital twin applications across industries. While many data-driven approaches aim to learn sy…
Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics
Mengjie Zhao, Cees Taal, Stephan Baggerohr +1
Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost,…
Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks
Mengjie Zhao, Cees Taal, Stephan Baggerohr +1
Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While beari…
Graph Neural Networks for Dynamic Modeling of Roller Bearing
Vinay Sharma, Jens Ravesloot, Cees Taal +1
In the presented work, we propose to apply the framework of graph neural networks (GNNs) to predict the dynamics of a rolling element bearing. This approach offers generalizability…
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Ismail Nejjar, Fabian Geissmann, Mengjie Zhao +2
Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the repres…
Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry
Karl Löwenmark, Cees Taal, Stephan Schnabel +2
In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability…
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…