138 citations · 418 across the 11 of their papers we have counts for
13 papers
Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction
Magnus Munk Jensen, Dorte Hammershøi, Rafał Wiśniewski +1
Fault detection is essential in industrial systems, enabling early identification of abnormal behaviour and improving safety, reliability, and operational efficiency. Modern system…
Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis
Jong Moon Ha, Olga Fink
Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible…
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…
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…
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…