23 citations · 23 across the 1 of their papers we have counts for
5 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…
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…
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
Gabriel Michau, Yang Hu, Thomas Palmé +1
Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it i…