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Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder

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Convolutional Neural Network Based Fault Detection for Rotating Machinery

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Wear particle classification considering particle overlapping

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A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network

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Amalgamation of Transfer Learning and Deep Convolutional Neural Network for Multiple Fault Detection in SCIM

inferredcites →https://doi.org/10.1109/jsen.2019.2949057openalex-snapshot

Knowledge Transfer for Rotary Machine Fault Diagnosis

inferredcites →https://doi.org/10.1109/robio49542.2019.8961677openalex-snapshot

Industrial Robot Rotate Vector Reducer Fault Detection Based on Hidden Markov Models

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Intelligent Diagnosis Using Continuous Wavelet Transform and Gauss Convolutional Deep Belief Network

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Ball Bearing Fault Diagnosis using Supervised and Unsupervised Machine Learning Methods

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Deep Learning Based Intelligent Industrial Fault Diagnosis Model

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A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

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Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM)

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Deep Learning-Based Fault Diagnosis of Servo Motor Bearing Using Attention-Guided Feature Aggregation Network

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REQDGAN‐TUA: A Reflection Equivariant Quantum Graph Attention Framework With Tactical Unit Optimization for Intelligent Fault Diagnosis in Industrial Robotics

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VGGNet19-LORC-DSHGAT: a graph topology optimized dual-stage attention network for multi-condition bearing fault diagnosis

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