138 citations · 615 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…