SOTAVerified

Fault Diagnosis

Papers

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Showing 176–200 of 375 papers

TitleStatusHype
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns—0
In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: a critical review—0
Integrated Approach of Gearbox Fault Diagnosis—0
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods—0
Integrating LLMs for Explainable Fault Diagnosis in Complex Systems—0
Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals—0
Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy—0
Co-training partial domain adaptation networks for industrial Fault Diagnosis—0
Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning—0
Interpretable Event Diagnosis in Water Distribution Networks—0
Interpreting What Typical Fault Signals Look Like via Prototype-matching—0
Joint Observer Gain and Input Design for Asymptotic Active Fault Diagnosis—0
KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks—0
Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosis—0
LD-RPMNet: Near-Sensor Diagnosis for Railway Point Machines—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Learning to better see the unseen: Broad-Deep Mixed Anti-Forgetting Framework for Incremental Zero-Shot Fault Diagnosis—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction—0
ABIGX: A Unified Framework for eXplainable Fault Detection and Classification—0
A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis—0
A class alignment method based on graph convolution neural network for bearing fault diagnosis in presence of missing data and changing working conditions—0
A Closer Look at Bearing Fault Classification Approaches—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis—0
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Benchmark Results

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