Search Results for author: Olga Fink

Found 42 papers, 11 papers with code

Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis

no code implementations31 May 2023 Jong Moon Ha, Olga Fink

CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively.

Domain Adaptation

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial

1 code implementation7 May 2023 Venkat Nemani, Luca Biggio, Xun Huan, Zhen Hu, Olga Fink, Anh Tran, Yan Wang, Xiaoping Du, Xiaoge Zhang, Chao Hu

In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.

Decision Making Management +1

Collective Relational Inference for learning physics-consistent heterogeneous particle interactions

no code implementations30 Apr 2023 Zhichao Han, Olga Fink, David S. Kammer

We evaluate the proposed methodology across several benchmark datasets and demonstrate that it is consistent with the underlying physics.

Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis

1 code implementation26 Apr 2023 Hao Lu, Adam Thelen, Olga Fink, Chao Hu, Simon Laflamme

To quantify dataset similarity between clients without explicitly sharing data, each client sets aside a local test dataset and evaluates the other clients' model prediction accuracy and uncertainty on this test dataset.

Federated Learning

Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions

no code implementations23 Apr 2023 Jiawei Xiong, Olga Fink, Jian Zhou, Yizhong Ma

In this study, a novel hybrid framework combining the controlled physics-informed data generation approach with a deep learning-based prediction model for prognostics is proposed.

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

1 code implementation23 Mar 2023 Ismail Nejjar, Qin Wang, Olga Fink

Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems.

regression Unsupervised Domain Adaptation

DARE-GRAM: Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

1 code implementation CVPR 2023 Ismail Nejjar, Qin Wang, Olga Fink

Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems.

regression Unsupervised Domain Adaptation

Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications

no code implementations28 Aug 2022 Katharina Rombach, Gabriel Michau, Kajan Ratnasabapathy, Lucian-Stefan Ancu, Wilfried Bürzle, Stefan Koller, Olga Fink

We evaluate how contrastive learning can be employed on supervised and unsupervised fault detection and diagnostics tasks given real condition monitoring datasets within a railway system - one image dataset from infrastructure assets and one time-series dataset from rolling stock assets.

Anomaly Detection Contrastive Learning +2

A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives

no code implementations27 Aug 2022 Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu

This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions.

A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies

no code implementations26 Aug 2022 Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu

In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.

Robust Time Series Denoising with Learnable Wavelet Packet Transform

no code implementations13 Jun 2022 Gaetan Frusque, Olga Fink

We show that the learnable wavelet packet transform has the learning capabilities of deep learning methods while maintaining the robustness of standard signal processing approaches.

Denoising Time Series Denoising

Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring

no code implementations11 May 2022 Baorui Dai, Gaëtan Frusque, Qi Li, Olga Fink

Therefore, only acoustic sensors (non-intrusive) need to be installed during the application phase, which is convenient and crucial for the condition monitoring of safety-critical infrastructure.

Denoising

Multi-agent Actor-Critic with Time Dynamical Opponent Model

no code implementations12 Apr 2022 Yuan Tian, Klaus-Rudolf Kladny, Qin Wang, Zhiwu Huang, Olga Fink

In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel \textit{Time Dynamical Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend to improve over time.

Multi-agent Reinforcement Learning

Continual Test-Time Domain Adaptation

1 code implementation CVPR 2022 Qin Wang, Olga Fink, Luc van Gool, Dengxin Dai

However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time.

Domain Adaptation

Learning Physics-Consistent Particle Interactions

no code implementations1 Feb 2022 Zhichao Han, David S. Kammer, Olga Fink

Access to the governing particle interaction law is fundamental for a complete understanding of such systems.

A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning

no code implementations20 Jan 2022 Yuan Tian, Minghao Han, Chetan Kulkarni, Olga Fink

Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems.

reinforcement-learning Reinforcement Learning (RL)

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

no code implementations20 Dec 2021 Pegah Rokhforoz, Olga Fink

This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment.

reinforcement-learning Reinforcement Learning (RL) +2

Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis

1 code implementation5 Jul 2021 Qin Wang, Cees Taal, Olga Fink

In this paper, we aim to overcome this limitation by integrating expert knowledge with domain adaptation in a synthetic-to-real framework for unsupervised fault diagnosis.

Domain Adaptation

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

no code implementations3 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.

Denoising Time Series Analysis

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.

BIG-bench Machine Learning reinforcement-learning +1

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.

Dimensionality Reduction One-Class Classification +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 +2

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.

reinforcement-learning Reinforcement Learning (RL)

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 Analysis 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.

Management Natural Language Understanding

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

3 code implementations7 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 Classification +2

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 +4

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 +3

Cannot find the paper you are looking for? You can Submit a new open access paper.