138 citations · 418 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…