DeepFedWT: A federated deep learning framework for fault detection of wind turbines
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
DeepFedWT: A federated deep learning framework for fault detection of wind turbines
Authors
Keywords
-
Journal
MEASUREMENT
Volume 199, Issue -, Pages 111529
Publisher
Elsevier BV
Online
2022-06-23
DOI
10.1016/j.measurement.2022.111529
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems
- (2021) Zhiwei Gao et al. Processes
- Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism
- (2021) Ling Xiang et al. MEASUREMENT
- Privacy-Preserving Federated Learning in Medical Diagnosis with Homomorphic Re-Encryption
- (2021) Hanchao Ku et al. COMPUTER STANDARDS & INTERFACES
- A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox
- (2021) Kai Zhang et al. MEASUREMENT
- Fault detection of complex planetary gearbox using acoustic signals
- (2021) Jiachi Yao et al. MEASUREMENT
- Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges
- (2021) Enrique Mármol Campos et al. Computer Networks
- Sparse dictionary learning based adversarial variational auto-encoders for fault identification of wind turbines
- (2021) Xiaobo Liu et al. MEASUREMENT
- A novel adaptive weighted kernel extreme learning machine algorithm and its application in wind turbine blade icing fault detection
- (2021) Ruining Tong et al. MEASUREMENT
- Wind turbine condition monitoring based on a novel multivariate state estimation technique
- (2020) Ziqi Wang et al. MEASUREMENT
- Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data
- (2020) Yanhua Pang et al. RENEWABLE ENERGY
- Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
- (2020) Wei Zhang et al. KNOWLEDGE-BASED SYSTEMS
- Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach
- (2020) Yi Liu et al. IEEE Internet of Things Journal
- DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
- (2020) Beibei Li et al. IEEE Transactions on Industrial Informatics
- Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data
- (2020) Zengqiang Yan et al. IEEE Journal of Biomedical and Health Informatics
- A Spatio-Temporal Multiscale Neural Network Approach for Wind Turbine Fault Diagnosis With Imbalanced SCADA Data
- (2020) Qun He et al. IEEE Transactions on Industrial Informatics
- Federated Machine Learning
- (2019) Qiang Yang et al. ACM Transactions on Intelligent Systems and Technology
- A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings
- (2019) Zepeng Liu et al. MEASUREMENT
- Res2Net: A New Multi-Scale Backbone Architecture
- (2019) Shang-Hua Gao et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
- (2018) Guoqian Jiang et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Sparse Time-Frequency Representation for Incipient Fault Diagnosis of Wind Turbine Drive Train
- (2018) Boyuan Yang et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information
- (2018) Guoqian Jiang et al. IEEE-ASME TRANSACTIONS ON MECHATRONICS
- Real-time monitoring, prognosis, and resilient control for wind turbine systems
- (2018) Zhiwei Gao et al. RENEWABLE ENERGY
- Deep Learning for fault detection in wind turbines
- (2018) Georg Helbing et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Machine learning methods for wind turbine condition monitoring: A review
- (2018) Adrian Stetco et al. RENEWABLE ENERGY
- Wind Turbine Gearbox Failure Identification With Deep Neural Networks
- (2017) Long Wang et al. IEEE Transactions on Industrial Informatics
- A survey of health monitoring systems for wind turbines
- (2015) Mathew L. Wymore et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Learning from Imbalanced Data
- (2009) Haibo He et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started