Search Results for author: Olga Fink

Found 20 papers, 4 papers with code

Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis

no code implementations5 Jul 2021 Qin Wang, Cees Taal, Olga Fink

Motivated by the fact that domain experts often have a relatively good understanding on how different fault types affect healthy signals, in the first step of the proposed framework, a synthetic fault dataset is generated by augmenting real vibration samples of healthy bearings.

Domain Adaptation

Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series

no code implementations3 May 2021 Gabriel Michau, Olga Fink

Using deep learning, we make this architecture fully learnable: both the wavelet bases and the wavelet coefficient denoising are learnable.

Denoising Time Series

Uncertainty-aware Remaining Useful Life predictor

no code implementations8 Apr 2021 Luca Biggio, Alexander Wieland, Manuel Arias Chao, Iason Kastanis, Olga Fink

Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate within its defined specifications.

Gaussian Processes

Battery Model Calibration with Deep Reinforcement Learning

no code implementations7 Dec 2020 Ajaykumar Unagar, Yuan Tian, Manuel Arias-Chao, Olga Fink

In this paper, we implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models.

Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data

no code implementations30 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.

Data Augmentation Fault Detection

Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer

no code implementations18 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.

Anomaly Detection Dimensionality Reduction +2

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

no code implementations13 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.

Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

1 code implementation ECCV 2020 Yuan Tian, Qin Wang, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.

Image Generation Neural Architecture Search

Real-Time Model Calibration with Deep Reinforcement Learning

no code implementations7 Jun 2020 Yuan Tian, Manuel Arias Chao, Chetan Kulkarni, Kai Goebel, Olga Fink

The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes.

Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms

no code implementations14 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.

Time Series Unsupervised Anomaly Detection

Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks

no code implementations14 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.

Anomaly Detection Classification +4

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications

no code implementations5 May 2020 Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe

Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding.

Natural Language Understanding

Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis

1 code implementation7 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.

Domain Adaptation

Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders

no code implementations28 Dec 2019 Manuel Arias Chao, Bryan T. Adey, Olga Fink

With this work, we propose training a variational autoencoder (VAE) with labeled and unlabeled samples while inducing implicit supervision on the latent representation of the healthy conditions.

Fault Detection One-class classifier +1

Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information

no code implementations22 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.

Domain Adaptation Fault Detection +2

Unsupervised Fault Detection in Varying Operating Conditions

no code implementations15 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.

Fault Detection Incremental Learning

Domain Adaptive Transfer Learning for Fault Diagnosis

no code implementations15 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.

Domain Adaptation Transfer Learning

Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals

1 code implementation12 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.

Dimensionality Reduction Fault Detection +1

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