69 citations · 97 across the 3 of their papers we have counts for
7 papers
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer
Gabriel Michau, Olga Fink
Anomaly Detectors are trained on healthy operating condition data and raise an alarm when the measured samples deviate from the training data distribution. This means that the samp…
Interpretable Detection of Partial Discharge in Power Lines with Deep Learning
Gabriel Michau, Chi-Ching Hsu, Olga Fink
Partial discharge (PD) is a common indication of faults in power systems, such as generators, and cables. These PD can eventually result in costly repairs and substantial power out…
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms
Gabriel Rodriguez Garcia, Gabriel Michau, Mélanie Ducoffe +2
The ability to detect anomalies in time series is considered highly valuable in numerous application domains. The sequential nature of time series objects is responsible for an add…
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…
Combining traffic counts and Bluetooth data for link-origin-destination matrix estimation in large urban networks: The Brisbane case study
Gabriel Michau, Nelly Pustelnik, Pierre Borgnat +3
Origin-Destination matrix estimation is a keystone for traffic representation and analysis. Traditionally estimated thanks to traffic counts, surveys and socio-economic models, rec…
Domain Adaptive Transfer Learning for Fault Diagnosis
Qin Wang, Gabriel Michau, Olga Fink
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving the…