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Found 5 papers, 1 papers with code
Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks
Since temperature and vibration signals exhibit vastly different dynamics, we propose Heterogeneous Temporal Graph Neural Networks (HTGNN), which explicitly models these signal types and their interactions for effective load prediction.
Graph Neural Networks for Dynamic Modeling of Roller Bearing
Through this approach, we demonstrate the effectiveness of the GNN-based method in accurately predicting the dynamics of rolling element bearings, highlighting its potential for real-time health monitoring of rotating machinery.
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features.
Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry
In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety.
Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
In this paper, we aim to overcome this limitation by integrating expert knowledge with domain adaptation in a synthetic-to-real framework for unsupervised fault diagnosis.
