On the public citation graph
Deep domain adversarial residual neural network for sustainable wind turbine cyber‐physical system fault diagnosis
0 signed · 51 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-13560-1_76openalex-snapshot
Domain Adaptive Neural Networks for Object Recognition
inferredcites →https://doi.org/10.1007/978-3-319-58347-1_10openalex-snapshot
Domain-Adversarial Training of Neural Networks
inferredcites →https://doi.org/10.1007/bf00344251openalex-snapshot
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
inferredcites →https://doi.org/10.1007/s00500-018-3256-0openalex-snapshot
Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks
inferredcites →https://doi.org/10.1016/j.apacoust.2014.08.016openalex-snapshot
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
inferredcites →https://doi.org/10.1016/j.energy.2017.03.038openalex-snapshot
Value of wind power – Implications from specific power
inferredcites →https://doi.org/10.1016/j.measurement.2017.07.017openalex-snapshot
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
inferredcites →https://doi.org/10.1016/j.measurement.2019.01.020openalex-snapshot
Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)
inferredcites →https://doi.org/10.1016/j.measurement.2019.106906openalex-snapshot
A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning
inferredcites →https://doi.org/10.1016/j.neucom.2016.11.063openalex-snapshot
An unsupervised deep domain adaptation approach for robust speech recognition
inferredcites →https://doi.org/10.1016/j.neucom.2018.05.083openalex-snapshot
Deep visual domain adaptation: A survey
inferredcites →https://doi.org/10.1016/j.renene.2019.06.103openalex-snapshot
An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples
inferredcites →https://doi.org/10.1016/j.ymssp.2018.12.051openalex-snapshot
An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings
inferredcites →https://doi.org/10.1038/nature11475openalex-snapshot
Opportunities and challenges for a sustainable energy future
inferredcites →https://doi.org/10.1063/1.4773826openalex-snapshot
Wavelet-based multifractal analysis of large scale wind turbine main bearing
inferredcites →https://doi.org/10.1088/1361-6501/ab3a59openalex-snapshot
An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis
inferredcites →https://doi.org/10.1088/1757-899x/677/3/032025openalex-snapshot
Research on Fault Diagnosis Method of Wind Turbine Bearing Based on Deep belief Network
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/access.2019.2944974openalex-snapshot
A Fault Diagnosis Method of Industrial Robot Rolling Bearing Based on Data Driven and Random Intuitive Fuzzy Decision
inferredcites →https://doi.org/10.1109/access.2019.2950240openalex-snapshot
A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition
inferredcites →https://doi.org/10.1109/access.2019.2954091openalex-snapshot
Rolling Bearing Initial Fault Detection Using Long Short-Term Memory Recurrent Network
inferredcites →https://doi.org/10.1109/access.2019.2958141openalex-snapshot
A Spare Support Vector Machine Based Fault Detection Strategy on Key Lubricating Interfaces of Axial Piston Pumps
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/iciea.2016.7603846openalex-snapshot
Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines
inferredcites →https://doi.org/10.1109/iecon.2014.7048774openalex-snapshot
Application of anomaly technique in wind turbine bearing fault detection
inferredcites →https://doi.org/10.1109/jsen.2017.2726011openalex-snapshot
Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests
inferredcites →https://doi.org/10.1109/jsen.2019.2939360openalex-snapshot
Fault Diagnosis for Electromechanical Drivetrains Using a Joint Distribution Optimal Deep Domain Adaptation Approach
inferredcites →https://doi.org/10.1109/msp.2014.2347059openalex-snapshot
Visual Domain Adaptation: A survey of recent advances
inferredcites →https://doi.org/10.1109/tie.2016.2580519openalex-snapshot
Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring
inferredcites →https://doi.org/10.1109/tie.2016.2627020openalex-snapshot
Deep Model Based Domain Adaptation for Fault Diagnosis
inferredcites →https://doi.org/10.1109/tsmc.2017.2754287openalex-snapshot
A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis
inferredcites →https://doi.org/10.1109/wcica.2012.6358418openalex-snapshot
Amplitude envelope analysis for feature extraction of direct-driven wind turbine bearing failure
inferredcites →https://doi.org/10.1155/2019/6943234openalex-snapshot
A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network
inferredcites →https://doi.org/10.1155/2019/6987427openalex-snapshot
Stability of the Evolutionary Game System and Control Strategies of Behavior Instability in Coal Mine Safety Management
inferredcites →https://doi.org/10.1177/0954406219875756openalex-snapshot
Raw vibration signal pattern recognition with automatic hyper-parameter-optimized convolutional neural network for bearing fault diagnosis
inferredcites →https://doi.org/10.1177/1077546311403185openalex-snapshot
Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing
inferredcites →https://doi.org/10.1177/1461348419889511openalex-snapshot
Deep learning for bearing fault diagnosis under different working loads and non-fault location point
inferredcites →https://doi.org/10.16513/j.cnki.10-1427/tk.2019.02.002openalex-snapshot
Smart Wind Power System Architecture
inferredcites →https://doi.org/10.3390/s17020425openalex-snapshot
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
inferredcites →https://doi.org/10.3390/sym11101212openalex-snapshot
Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform
inferredcites →https://doi.org/10.48550/arxiv.1504.01255openalex-snapshot
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4683457openalex-extracted
The Comprehensive Review of Wind Turbine Main Bearing Failures, Reinforcement Measures and Condition Monitoring
inferred← citeshttps://doi.org/10.1002/spe.3017openalex-snapshot
Software and hardware co‐design for sustainable cyber‐physical systems
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111484openalex-snapshot
A review on data-driven prognostics and health management for wind turbine systems
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112753openalex-snapshot
Multi-scale dynamic spatio-temporal graph network for anomaly detection of wind turbine main bearing under time-varying conditions
inferred← citeshttps://doi.org/10.1016/j.psep.2023.03.012openalex-snapshot
Analysis of safety and security challenges and opportunities related to cyber-physical systems
inferred← citeshttps://doi.org/10.1109/qrs-c63300.2024.00073openalex-snapshot
Fault Diagnosis Using Channelized Encoder-Decoder Frame for Distributed Redundant Inertial Navigation System
inferred← citeshttps://doi.org/10.1109/tim.2023.3276007openalex-snapshot
Deep Adversarial Hybrid Domain-Adaptation Network for Varying Working Conditions Fault Diagnosis of High-Speed Train Bogie
inferred← citeshttps://doi.org/10.1109/tim.2024.3472809openalex-snapshot
Small Sample Fault Diagnosis Based on Intrinsic Characteristic Encouraged Network for Unmanned Aerial Vehicle Sensors
inferred← citeshttps://doi.org/10.1109/yac66630.2025.11149984openalex-snapshot
Fuzzy Inference and Temporal Logic Based Reliable Graph Neural Network for Industrial Fault Diagnosis
inferred← citeshttps://doi.org/10.3390/s23167263openalex-snapshot
Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.