SOTAVerified

Fault Diagnosis

Papers

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Showing 76–100 of 375 papers

TitleStatusHype
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
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines—0
Real-time and Downtime-tolerant Fault Diagnosis for Railway Turnout Machines (RTMs) Empowered with Cloud-Edge Pipeline Parallelism—0
Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring DataCode1
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
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
Consultation on Industrial Machine Faults with Large language Models—0
Canonical Correlation Guided Deep Neural Network—0
Demystifying and Extracting Fault-indicating Information from Logs for Failure DiagnosisCode1
Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems—0
VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis—0
AI-driven Transformer Model for Fault Prediction in Non-Linear Dynamic Automotive System—0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis—0
BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal RepresentationCode2
Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks—0
The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
SRTFD: Scalable Real-Time Fault Diagnosis through Online Continual Learning—0
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Benchmark Results

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