SOTAVerified

Fault Diagnosis

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 375 papers

TitleStatusHype
An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning—0
Towards Fault Diagnosis in Induction Motor using Fractional Fourier Transform—0
SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults—0
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
TelApart: Differentiating Network Faults from Customer-Premise Faults in Cable Broadband Networks—0
A Review of Intelligent Device Fault Diagnosis Technologies Based on Machine Vision—0
Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks—0
Granular Ball K-Class Twin Support Vector Classifier—0
Model-Agnostic Meta-Learning for Fault Diagnosis of Induction Motors in Data-Scarce Environments with Varying Operating Conditions and Electric Drive Noise—0
Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives—0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Residual Attention Single-Head Vision Transformer Network for Rolling Bearing Fault Diagnosis in Noisy Environments—0
NonSysId: A nonlinear system identification package with improved model term selection for NARMAX models—0
Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis—0
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module—0
Research on fault diagnosis of nuclear power first-second circuit based on hierarchical multi-granularity classification network—0
Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm—0
Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals—0
LLM-based Framework for Bearing Fault Diagnosis—0
Real-time and Downtime-tolerant Fault Diagnosis for Railway Turnout Machines (RTMs) Empowered with Cloud-Edge Pipeline Parallelism—0
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Co-training partial domain adaptation networks for industrial Fault Diagnosis—0
Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM—0
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DANNAccuray80.22—Unverified
2LSTMAccuray61.56—Unverified