138 citations · 615 across the 16 of their papers we have counts for
17 papers
Learning Informative Health Indicators Through Unsupervised Contrastive Learning
Katharina Rombach, Gabriel Michau, Wilfried Bürzle +2
Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health stat…
Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation
Katharina Rombach, Gabriel Michau, Olga Fink
New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. W…
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…
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series
Gabriel Michau, Gaetan Frusque, Olga Fink
High-Frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep learning tools are designed for inputs of fix…
Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines
Gabriel Rodriguez Garcia, Gabriel Michau, Herbert H. Einstein +1
In tunnel construction projects, delays induce high costs. Thus, tunnel boring machines (TBM) operators aim for fast advance rates, without safety compromise, a difficult mission i…
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…
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…
Unsupervised Fault Detection in Varying Operating Conditions
Gabriel Michau, Olga Fink
Training data-driven approaches for complex industrial system health monitoring is challenging. When data on faulty conditions are rare or not available, the training has to be per…
Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information
Gabriel Michau, Olga Fink
The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore…
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…
A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation
Gabriel Michau, Nelly Pustelnik, Pierre Borgnat +4
Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic coun…