Search Results for author:
Found 63 papers, 16 papers with code
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.
Domain Adaptive Transfer Learning for Fault Diagnosis
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.
Unsupervised Fault Detection in Varying Operating Conditions
Two approaches rely on the data from the unit to be monitored only: the baseline is trained on the early life of the unit.
Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information
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.
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders
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.
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training.
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding.
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms
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.
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks
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.
Real-Time Model Calibration with Deep Reinforcement Learning
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.
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
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.
Ranked #13 on
Image Generation
on STL-10
Interpretable Detection of Partial Discharge in Power Lines with Deep Learning
It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs.
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer
A solution to this problem is to perform unsupervised transfer learning (UTL), to transfer complementary data between different units.
Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data
In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space.
Battery Model Calibration with Deep Reinforcement Learning
In this paper, we implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models.
Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines
In tunnel construction projects, delays induce high costs.
Uncertainty-aware Remaining Useful Life predictor
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.
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
However, such a supervision is not always available.
Ranked #15 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
(using extra training data)
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series
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.
Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
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.
Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units
This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment.
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning
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.
Learning Physics-Consistent Particle Interactions
Access to the governing particle interaction law is fundamental for a complete understanding of such systems.
Continual Test-Time Domain Adaptation
However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time.
Multi-agent Actor-Critic with Time Dynamical Opponent Model
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.
Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring
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.
Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction
Electrochemical batteries are ubiquitous devices in our society.
Robust Time Series Denoising with Learnable Wavelet Packet Transform
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.
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets
This information on the system topology helps the agents to improve their bidding strategies and increase the profit.
Distributed Optimization
Multi-agent Reinforcement Learning
+2
Vacuum Circuit Breaker Closing Time Key Moments Detection via Vibration Monitoring: A Run-to-Failure Study
A key to ensure a reliable operation of CBs is to monitor their condition.
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies
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.
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives
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.
Learning Informative Health Indicators Through Unsupervised Contrastive Learning
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.
DARE-GRAM: Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems.
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features.
DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems.
Filter-informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis
Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches.
Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions
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.
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis
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.
Collective Relational Inference for learning heterogeneous interactions
First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time.
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
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.
Gemtelligence: Accelerating Gemstone classification with Deep Learning
The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dollars.
Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis
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.
Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach
In this paper, we enhance the quality assurance of manufacturing utilizing FMCW radar as a non-destructive sensing modality.
Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
We validate the effectiveness of the proposed SFDANN method based on two fault diagnosis cases: one involving fault diagnosis of bearings in noisy environments and another involving fault diagnosis of slab tracks in a train-track-bridge coupling vibration system, where the transfer task involves transferring from numerical simulations to field measurements.
DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems
We rigorously evaluated DyEdgeGAT using both a synthetic dataset, simulating varying levels of fault severity, and a real-world industrial-scale multiphase flow facility benchmark with diverse fault types under varying operating conditions and detection complexities.
Ranked #1 on
Unsupervised Anomaly Detection
on PRONTO
A Novel Unsupervised Graph Wavelet Autoencoder for Mechanical System Fault Detection
To overcome these limitations, we propose two graph neural network models: the graph wavelet autoencoder (GWAE), and the graph wavelet variational autoencoder (GWVAE).
Deep Koopman Operator-based degradation modelling
In this work, we demonstrate the successful extension of the previously proposed Deep Koopman Operator approach to learn the dynamics of industrial systems by transforming them into linearized coordinate systems, resulting in a latent representation that provides sufficient information for estimating the system's remaining useful life.
A Comparison of Residual-based Methods on Fault Detection
Fault detection is achieved by applying a threshold that is determined based on the healthy condition.
Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems
The use of Internet of Things (IoT) sensors for air pollution monitoring has significantly increased, resulting in the deployment of low-cost sensors.
Graph Neural Networks for Dynamic Modeling of Roller Bearing
Through this approach, we demonstrate the effectiveness of the GNN-based method in accurately predicting the dynamics of rolling element bearings, highlighting its potential for real-time health monitoring of rotating machinery.
From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring
This paper proposes applying this methodology to segment and monitor surface cracks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions.
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.
Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
However, deep learning models usually only perform well on the data distribution they have been trained on.
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion
The Health Index (HI) is crucial for evaluating system health, aiding tasks like anomaly detection and predicting remaining useful life for systems demanding high safety and reliability.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression
Our approach serves a dual purpose: providing a measure of confidence in predictions and acting as a regularization of the embedding space.
Sym-Q: Adaptive Symbolic Regression via Sequential Decision-Making
Our results demonstrate that Sym-Q excels not only in recovering underlying mathematical structures but also uniquely learns to efficiently refine the output expression based on reward signals, thereby discovering underlying expressions.
ThermoNeRF: Multimodal Neural Radiance Fields for Thermal Novel View Synthesis
Thermal scene reconstruction exhibit great potential for ap- plications across a broad spectrum of fields, including building energy consumption analysis and non-destructive testing.
Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks
Since temperature and vibration signals exhibit vastly different dynamics, we propose Heterogeneous Temporal Graph Neural Networks (HTGNN), which explicitly models these signal types and their interactions for effective load prediction.
Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity
PSH are complex, highly interconnected systems encompassing electrical and hydraulic subsystems, each characterized by their respective underlying networks that can individually be represented as graphs.
Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things
To address this limitation, we adopt Graph Neural Networks (GNNs), renowned for their ability to effectively capture the complex relationships between sensor measurements.
Prescribing Optimal Health-Aware Operation for Urban Air Mobility with Deep Reinforcement Learning
This paper addresses the joint problem of mission planning and health-aware real-time control of opera-tional parameters to prescriptively control the duration of one discharge cycle of the battery pack.
