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Fault Diagnosis

Papers

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Showing 301–325 of 375 papers

TitleStatusHype
Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN—0
VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis—0
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis—0
Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosis—0
Learning to better see the unseen: Broad-Deep Mixed Anti-Forgetting Framework for Incremental Zero-Shot Fault Diagnosis—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
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module—0
A comprehensive review on convolutional neural network in machine fault diagnosis—0
A Comprehensive Survey of Belief Rule Base (BRB) Hybrid Expert system: Bridging Decision Science and Professional Services—0
A Cost-Sensitive Deep Belief Network for Imbalanced Classification—0
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
A crayfish-optimized wavelet filter and its application to fault diagnosis—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
A data-driven approach to UIO-based fault diagnosis—0
Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition—0
A fault detection scheme for PV panels in large scale PV stations with complex installation conditions—0
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid—0
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Benchmark Results

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