no code implementations • 28 Aug 2022 • Katharina Rombach, Gabriel Michau, Wilfried Bürzle, Stefan Koller, Olga Fink
Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods.
no code implementations • 3 May 2021 • Gabriel Michau, Gaetan Frusque, Olga Fink
In this paper, we propose a fully unsupervised deep learning framework that is able to extract a meaningful and sparse representation of raw HF signals.
no code implementations • 7 Jan 2021 • Gabriel Rodriguez Garcia, Gabriel Michau, Herbert H. Einstein, Olga Fink
In tunnel construction projects, delays induce high costs.
no code implementations • 30 Sep 2020 • Katharina Rombach, Gabriel Michau, Olga Fink
In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space.
no code implementations • 18 Aug 2020 • Gabriel Michau, Olga Fink
A solution to this problem is to perform unsupervised transfer learning (UTL), to transfer complementary data between different units.
no code implementations • 13 Aug 2020 • Gabriel Michau, Chi-Ching Hsu, Olga Fink
It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs.
no code implementations • 14 May 2020 • Oliver Ammann, Gabriel Michau, Olga Fink
We demonstrate that mixing kervolutional with convolutional layers in the encoder is more sensitive to variations in the input data and is able to detect anomalous time series in a better way.
no code implementations • 14 May 2020 • Gabriel Rodriguez Garcia, Gabriel Michau, Mélanie Ducoffe, Jayant Sen Gupta, Olga Fink
Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series.
3 code implementations • 7 Jan 2020 • Qin Wang, Gabriel Michau, Olga Fink
We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training.
no code implementations • 22 Jul 2019 • Gabriel Michau, Olga Fink
In the early life of the system, the collected data is probably not representative of future operating conditions, making it challenging to train a robust model.
no code implementations • 15 Jul 2019 • Gabriel Michau, Olga Fink
Two approaches rely on the data from the unit to be monitored only: the baseline is trained on the early life of the unit.
no code implementations • 15 May 2019 • 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.
1 code implementation • 12 Oct 2018 • Gabriel Michau, Yang Hu, Thomas Palmé, Olga Fink
The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.