SOTAVerified

Fault Diagnosis

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 375 papers

TitleStatusHype
UniFault: A Fault Diagnosis Foundation Model from Bearing Data—0
An End-to-End Comprehensive Gear Fault Diagnosis Method Based on Multi-Scale Feature-Level Fusion Strategy—0
Bearing fault diagnosis based on multi-scale spectral images and convolutional neural network—0
Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network—0
Enhancing Fault Detection and Isolation in an All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal—0
Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain—0
Semi-Supervised Co-Training of Time and Time-Frequency Models: Application to Bearing Fault Diagnosis—0
UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data—0
Robust Unsupervised Fault Diagnosis For High-Dimensional Nonlinear Noisy Data—0
Fault Localization and State Estimation of Power Grid under Parallel Cyber-Physical Attacks—0
Efficient Fault Diagnosis in Lithium-Ion Battery Packs: A Structural Approach with Moving Horizon Estimation—0
Sparse Spectrahedral Shadows for State Estimation and Reachability Analysis: Set Operations, Validations and Order Reductions—0
Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network—0
Network Tomography with Path-Centric Graph Neural Network—0
Deep Subspace Learning for Surface Anomaly Classification Based on 3D Point Cloud Data—0
A crayfish-optimized wavelet filter and its application to fault diagnosis—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Hybrid Resolver Model Generalization for Fault Condition Modeling: A Promising Tool for Reliability Study—0
AI Explainability for Power Electronics: From a Lipschitz Continuity Perspective—0
Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge DistillationCode0
Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosis—0
A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset—0
Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?—0
Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights, and ProspectsCode0
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty—0
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Benchmark Results

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