Research graph
References from Probing an industrial domain knowledge-enhanced transfer graph neural network-driven fault diagnosis approach for machine tools. Local targets link to admitted publications; unresolved targets remain external evidence.
Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities
10.1016/j.jmsy.2023.11.001 · 2024 · External reference
Failure analysis of CNC machines due to human errors: An integrated IT2F-MCDM-based FMEA approach
10.1016/j.engfailanal.2021.105768 · 2021 · External reference
Remote Diagnosis and Detection Technology for Electrical Control of Intelligent Manufacturing CNC Machine Tools
2022 · External reference
Sequential Bayesian Inference of the GTN damage model using multimodal experimental data
2026 · External reference
Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
10.1038/s41598-024-79151-2 · 2025 · External reference
Z. AI-Sharify, A data fusion analysis and random forest learning for enhanced control and failure diagnosis in rotating machinery
10.1007/s11668-024-02075-6 · 2024 · External reference
Simultaneous fault diagnosis based on multiple kernel sup
10.1002/ese3.1058 · 2022 · External reference
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
10.1016/j.ymssp.2021.108653 · 2022 · External reference
An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
10.1016/j.ymssp.2023.110534 · 2023 · External reference
Cross-domain few-shot fault diagnosis based on meta-learning and domain adversarial graph convolutional network
10.1016/j.engappai.2024.108970 · 2024 · External reference
Intelligent Fault Diagnosis for CNC Through the Integration of Large Language Models and Domain Knowledge Graphs
10.1016/j.eng.2025.04.003 · 2025 · External reference
A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults
10.1016/j.dt.2025.04.014 · 2025 · External reference
A graph neural network-based bearing fault detection method
10.1038/s41598-023-32369-y · 2023 · External reference
The construction and application of knowledge graph-based fault diagnostic system of CNC machine tool
10.1117/12.2679303 · 2023 · External reference
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
2023 · External reference
Cases Integration System for Fault Diagnosis of CNC Machine Tools Based on Knowledge Graph
10.54097/ajst.v5i1.5664 · 2023 · External reference
An automatic machine fault identification method using the knowledge graph–embedded large language model
10.1007/s00170-025-15555-2 · 2025 · External reference
Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing
10.1007/s00170-024-12998-x · 2024 · External reference
Unresolved reference
External reference
A physics-informed deep learning approach for bearing fault detection
10.1016/j.engappai.2021.104295 · 2021 · External reference
Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis
10.1016/j.knosys.2023.110891 · 2023 · External reference
Translating embedding for modeling multi-relational data
2013 · External reference
A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism
10.3390/en16135230 · 2023 · External reference
Long Short-Term Memory
10.1162/neco.1997.9.8.1735 · 1997 · External reference
Effective Approaches to Attention-based Neural Machine Translation
10.18653/v1/d15-1166 · 2015 · External reference
An industrial fault diagnosis method based on graph attention network
10.1021/acs.iecr.4c02220 · 2024 · External reference
Spatial-temporal bearing fault detection using graph attention networks and LSTM
2024 · External reference
A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT
10.3390/e25060889 · 2023 · External reference
Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection
10.1016/j.measurement.2021.110506 · 2022 · External reference
Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
2023 · External reference
A dynamic collaborative adversarial domain adaptation network for unsupervised rotating machinery fault diagnosis
2024 · External reference
LLM-based framework for bearing fault diagnosis
10.1016/j.ymssp.2024.112127 · 2025 · External reference
Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning
2020 · External reference
A Kernel Two-Sample Test
2012 · External reference
Domain-Adversarial Training of Neural Networks
10.1007/978-3-319-58347-1_10 · 2017 · External reference
Fault detection based on U-Net and GNN integration
10.1016/j.jsg.2025.105426 · 2025 · External reference
An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
10.1016/j.ymssp.2023.110534 · 2023 · External reference
A novel fault diagnosis and accurate localization method for a power system based on graphsage algorithm
10.3390/electronics14061219 · 2025 · External reference
Hybrid Deep LSTM-GAT network with mechanism information for prediction of mach number
2025 · External reference
Simultaneous fault diagnosis based on multiple kernel sup
10.1002/ese3.1058 · ExternalCitation · doi-reference
Domain-Adversarial Training of Neural Networks
10.1007/978-3-319-58347-1_10 · ExternalCitation · doi-reference
Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing
10.1007/s00170-024-12998-x · ExternalCitation · doi-reference
An automatic machine fault identification method using the knowledge graph–embedded large language model
10.1007/s00170-025-15555-2 · ExternalCitation · doi-reference
Z. AI-Sharify, A data fusion analysis and random forest learning for enhanced control and failure diagnosis in rotating machinery
10.1007/s11668-024-02075-6 · ExternalCitation · doi-reference
A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults
10.1016/j.dt.2025.04.014 · ExternalCitation · doi-reference
Intelligent Fault Diagnosis for CNC Through the Integration of Large Language Models and Domain Knowledge Graphs
10.1016/j.eng.2025.04.003 · ExternalCitation · doi-reference
A physics-informed deep learning approach for bearing fault detection
10.1016/j.engappai.2021.104295 · ExternalCitation · doi-reference
Cross-domain few-shot fault diagnosis based on meta-learning and domain adversarial graph convolutional network
10.1016/j.engappai.2024.108970 · ExternalCitation · doi-reference
Failure analysis of CNC machines due to human errors: An integrated IT2F-MCDM-based FMEA approach
10.1016/j.engfailanal.2021.105768 · ExternalCitation · doi-reference
Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities
10.1016/j.jmsy.2023.11.001 · ExternalCitation · doi-reference
Fault detection based on U-Net and GNN integration
10.1016/j.jsg.2025.105426 · ExternalCitation · doi-reference
Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis
10.1016/j.knosys.2023.110891 · ExternalCitation · doi-reference
Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection
10.1016/j.measurement.2021.110506 · ExternalCitation · doi-reference
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
10.1016/j.ymssp.2021.108653 · ExternalCitation · doi-reference
An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
10.1016/j.ymssp.2023.110534 · ExternalCitation · doi-reference
LLM-based framework for bearing fault diagnosis
10.1016/j.ymssp.2024.112127 · ExternalCitation · doi-reference
An industrial fault diagnosis method based on graph attention network
10.1021/acs.iecr.4c02220 · ExternalCitation · doi-reference
A graph neural network-based bearing fault detection method
10.1038/s41598-023-32369-y · ExternalCitation · doi-reference
Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
10.1038/s41598-024-79151-2 · ExternalCitation · doi-reference
The construction and application of knowledge graph-based fault diagnostic system of CNC machine tool
10.1117/12.2679303 · ExternalCitation · doi-reference
Long Short-Term Memory
10.1162/neco.1997.9.8.1735 · ExternalCitation · doi-reference
Effective Approaches to Attention-based Neural Machine Translation
10.18653/v1/d15-1166 · ExternalCitation · doi-reference
A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT
10.3390/e25060889 · ExternalCitation · doi-reference
A novel fault diagnosis and accurate localization method for a power system based on graphsage algorithm
10.3390/electronics14061219 · ExternalCitation · doi-reference
A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism
10.3390/en16135230 · ExternalCitation · doi-reference
Cases Integration System for Fault Diagnosis of CNC Machine Tools Based on Knowledge Graph
10.54097/ajst.v5i1.5664 · ExternalCitation · doi-reference
