138 citations · 615 across the 16 of their papers we have counts for
6 papers · 1 filter
Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data
Katharina Rombach, Gabriel Michau, Olga Fink
Mislabeled samples are ubiquitous in real-world datasets as rule-based or expert labeling is usually based on incorrect assumptions or subject to biased opinions. Neural networks c…
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…
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks
Oliver Ammann, Gabriel Michau, Olga Fink
Recently, with the development of deep learning, end-to-end neural network architectures have been increasingly applied to condition monitoring signals. They have demonstrated supe…
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…