At IMOS, our research encompasses both methodological contributions and practical applications in Prognostics and Health Management. Our methodological advancements span across diverse fields such as PHM, computer vision, and signal processing, while our applications address various domains, including energy and transportation. Through our work, we strive to bridge the gap between theoretical developments and real-world challenges.
List of all Publications
Prescribing optimal health-aware operation for urban air mobility with deep reinforcement learning
M. Montazeri; C. S. Kulkarni; O. Fink
Reliability Engineering & System Safety. 2025. Vol. 259, p. 1 – 14. DOI : 10.1016/j.ress.2025.110897. Power for AI and AI for Power: The Infinite Entanglement Between Artificial Intelligence and Power Electronics Systems
M. Chen; H. Cui; F. Blaabjerg; L. Lorenz; R. Hellinger et al.
IEEE POWER ELECTRONICS MAGAZINE. 2025. Vol. 12, num. 1, p. 37 – 43. DOI : 10.1109/MPEL.2024.3524742. Exploiting semantic scene reconstruction for estimating building envelope characteristics
C. Xu; M. Mielle; A. Laborde; A. Waseem; F. Forest et al.
Building and Environment. 2025. DOI : 10.1016/j.buildenv.2025.112731. Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-training Strategies and Performance Insights
Y. Hao; F. Forest; O. Fink
2025. 18th European Conference on Computer Vision, Milan, Italy, 2024-09-29 – 2024-10-04. p. 196 – 213. DOI : 10.1007/978-3-031-72949-2_12. Graph Neural Networks With Adaptive Structures
Z. Zhang; S. Lu; Z. Huang; Z. Zhao
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. 2025. Vol. 19, num. 1, p. 181 – 194. DOI : 10.1109/JSTSP.2024.3485892. Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models
I. Nejjar / O. Fink (Dir.)
Lausanne, EPFL, 2025. K-space physics-informed neural network (k-PINN) for compressed spectral mapping and efficient inversion of vibrations in thin composite laminates
S. Hedayatrasa; O. Fink; W. Van Paepegem; M. Kersemans
Mechanical Systems and Signal Processing. 2025. Vol. 223. DOI : 10.1016/j.ymssp.2024.111920. Calibrated Adaptive Teacher for Domain-Adaptive Intelligent Fault Diagnosis
F. Forest; O. Fink
SENSORS. 2024. Vol. 24, num. 23. DOI : 10.3390/s24237539. Digital Twin Generalization with Meta and Geometric Deep Learning
R. Theiler; O. Fink
2024. 16 Conference of the Prognostics and Health Management Society, Nashville, United States, 2024-11-10 – 2024-11-15. DOI : 10.36001/phmconf.2024.v16i1.4176. Assessing Aircraft Engine Wear through Simulation Techniques
A. Madane; J. Lacaille; F. Forest; H. Azzag; M. Lebbah
2024. 16 Conference of the Prognostics and Health Management Society, Nashville, United States, 2024-11-10 – 2024-11-15. DOI : 10.36001/phmconf.2024.v16i1.3924. Deep Koopman Operator-based degradation modelling
S. Garmaev; O. Fink
Reliability Engineering & System Safety. 2024. Vol. 251. DOI : 10.1016/j.ress.2024.110351. Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation
K. Faghih Niresi; L. Kühn; G. Frusque; O. Fink
2024. 32nd European Signal Processing Conference, Lyon, France, 2024-08-26 – 2024-08-30. p. 2467 – 2471. DOI : 10.23919/eusipco63174.2024.10715365. Robust time series denoising with learnable wavelet packet transform
G. Frusque; O. Fink
Advanced Engineering Informatics. 2024. Vol. 62, p. 102669. DOI : 10.1016/j.aei.2024.102669. IM-Context: In-Context Learning for Imbalanced Regression Tasks
I. Nejjar; F. Ahmed; O. Fink
Transactions on Machine Learning Research. 2024. From classification to segmentation with explainable AI: A study on crack detection and growth monitoring
F. Forest; H. Porta; D. Tuia; O. Fink
Automation in Construction. 2024. Vol. 165, p. 105497. DOI : 10.1016/j.autcon.2024.105497. Machine condition monitoring, fault diagnosis/prognosis, and maintenance
H. Cao; O. Fink; F. Gu
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE. 2024. DOI : 10.1177/09544062241255987. DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems
M. Zhao; O. Fink
IEEE Internet of Things Journal. 2024. Vol. 11, num. 13, p. 22950 – 22965. DOI : 10.1109/JIOT.2024.3381002. Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks
M. Zhao; C. Taal; S. Baggerohr; O. Fink
2024. 8th European Conference of the Prognostics and Health Management Society, Prague, Czech, 2024-07-03 – 2024-07-05. DOI : 10.36001/phme.2024.v8i1.3998.
