23 citations · 85 across the 14 of their papers we have counts for
14 papers
Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring
Wenyang Hu, Gaetan Frusque, Tianyang Wang +2
Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may…
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation
Keivan Faghih Niresi, Lucas Kuhn, Gaëtan Frusque +1
Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby fa…
IM-Context: In-Context Learning for Imbalanced Regression Tasks
Ismail Nejjar, Faez Ahmed, Olga Fink
Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gr…
Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression
Ismail Nejjar, Gaetan Frusque, Florent Forest +1
Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature align…
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion
Gaëtan Frusque, Ismail Nejjar, Majid Nabavi +1
The Health Index (HI) is crucial for evaluating system health and is important for tasks like anomaly detection and Remaining Useful Life (RUL) prediction of safety-critical system…
A Comparison of Residual-based Methods on Fault Detection
Chi-Ching Hsu, Gaetan Frusque, Olga Fink
An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this healt…
Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
Baorui Dai, Gaëtan Frusque, Tianfu Li +2
The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operationa…
Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach
Gaëtan Frusque, Daniel Mitchell, Jamie Blanche +2
The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs and lead to severe and disastr…
DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
Ismail Nejjar, Qin Wang, Olga Fink
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent work…
Vacuum Circuit Breaker Closing Time Key Moments Detection via Vibration Monitoring: A Run-to-Failure Study
Chi-Ching Hsu, Gaetan Frusque, Mahir Muratovic +2
Circuit breakers (CBs) play an important role in modern society because they make the power transmission and distribution systems reliable and resilient. Therefore, it is important…
Robust Time Series Denoising with Learnable Wavelet Packet Transform
Gaetan Frusque, Olga Fink
Signal denoising is a key preprocessing step for many applications, as the performance of a learning task is closely related to the quality of the input data. In this paper, we app…
Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring
Baorui Dai, Gaëtan Frusque, Qi Li +1
Acoustic monitoring has recently shown great potential in the diagnosis of infrastructure condition. However, due to the severe noise interference in acoustic signals, meaningful f…
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms
Gaetan Frusque, Olga Fink
Capturing high-frequency data concerning the condition of complex systems, e.g. by acoustic monitoring, has become increasingly prevalent. Such high-frequency signals typically con…
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection
Gaetan Frusque, Gabriel Michau, Olga Fink
Acoustic monitoring for machine fault detection is a recent and expanding research path that has already provided promising results for industries. However, it is impossible to col…