Probing an industrial domain knowledge-enhanced transfer graph neural network-driven fault diagnosis approach for machine tools

A novel fault diagnosis and accurate localization method for a power system based on graphsage algorithm

10.3390/electronics14061219 · doi-reference

Fault detection based on U-Net and GNN integration

10.1016/j.jsg.2025.105426 · doi-reference

Domain-Adversarial Training of Neural Networks

10.1007/978-3-319-58347-1_10 · doi-reference

LLM-based framework for bearing fault diagnosis

10.1016/j.ymssp.2024.112127 · doi-reference

Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection

10.1016/j.measurement.2021.110506 · doi-reference

A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT

10.3390/e25060889 · doi-reference

An industrial fault diagnosis method based on graph attention network

10.1021/acs.iecr.4c02220 · doi-reference

Effective Approaches to Attention-based Neural Machine Translation

10.18653/v1/d15-1166 · doi-reference

Long Short-Term Memory

10.1162/neco.1997.9.8.1735 · doi-reference

A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism

10.3390/en16135230 · doi-reference

Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis

10.1016/j.knosys.2023.110891 · doi-reference

A physics-informed deep learning approach for bearing fault detection

10.1016/j.engappai.2021.104295 · doi-reference

Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing

10.1007/s00170-024-12998-x · doi-reference

An automatic machine fault identification method using the knowledge graph–embedded large language model

10.1007/s00170-025-15555-2 · doi-reference

Cases Integration System for Fault Diagnosis of CNC Machine Tools Based on Knowledge Graph

10.54097/ajst.v5i1.5664 · doi-reference

The construction and application of knowledge graph-based fault diagnostic system of CNC machine tool

10.1117/12.2679303 · doi-reference

A graph neural network-based bearing fault detection method

10.1038/s41598-023-32369-y · 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 · 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 · doi-reference

Cross-domain few-shot fault diagnosis based on meta-learning and domain adversarial graph convolutional network

10.1016/j.engappai.2024.108970 · 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 · 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 · doi-reference

Simultaneous fault diagnosis based on multiple kernel sup

10.1002/ese3.1058 · 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 · 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 · doi-reference

Failure analysis of CNC machines due to human errors: An integrated IT2F-MCDM-based FMEA approach

10.1016/j.engfailanal.2021.105768 · doi-reference

Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

10.1016/j.jmsy.2023.11.001 · doi-reference