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Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s00170-018-2167-7openalex-snapshot
Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method
inferredcites →https://doi.org/10.1016/j.eswa.2013.12.026openalex-snapshot
An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks
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Graph embedding techniques, applications, and performance: A survey
inferredcites →https://doi.org/10.1016/j.neucom.2005.12.126openalex-snapshot
Extreme learning machine: Theory and applications
inferredcites →https://doi.org/10.1016/j.neucom.2016.12.027openalex-snapshot
Generalized extreme learning machine autoencoder and a new deep neural network
inferredcites →https://doi.org/10.1016/j.neucom.2018.07.038openalex-snapshot
Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis
inferredcites →https://doi.org/10.1109/access.2019.2894014openalex-snapshot
Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation
inferredcites →https://doi.org/10.1109/isit.2004.1365067openalex-snapshot
Jensen-Shannon divergence and Hilbert space embedding
inferredcites →https://doi.org/10.1109/msp.2012.2205597openalex-snapshot
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
inferredcites →https://doi.org/10.1109/tie.2017.2745473openalex-snapshot
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
inferredcites →https://doi.org/10.1109/tie.2019.2905830openalex-snapshot
Learning Representations With Local and Global Geometries Preserved for Machine Fault Diagnosis
inferredcites →https://doi.org/10.1109/tii.2013.2243743openalex-snapshot
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
inferredcites →https://doi.org/10.1109/tii.2018.2793246openalex-snapshot
Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data
inferredcites →https://doi.org/10.1109/tii.2019.2943898openalex-snapshot
Deep Residual Shrinkage Networks for Fault Diagnosis
inferredcites →https://doi.org/10.1109/tii.2020.3034189openalex-snapshot
Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data
inferredcites →https://doi.org/10.1109/tim.2017.2664599openalex-snapshot
Early Fault Diagnosis of Rotating Machinery by Combining Differential Rational Spline-Based LMD and K–L Divergence
inferredcites →https://doi.org/10.1109/tim.2019.2913058openalex-snapshot
A Combined Polynomial Chirplet Transform and Synchroextracting Technique for Analyzing Nonstationary Signals of Rotating Machinery
inferredcites →https://doi.org/10.1109/tmech.2020.3004589openalex-snapshot
Intelligent Fault Diagnosis of Multichannel Motor–Rotor System Based on Multimanifold Deep Extreme Learning Machine
inferredcites →https://doi.org/10.1109/tmech.2020.3025615openalex-snapshot
A Two-Stage Transfer Adversarial Network for Intelligent Fault Diagnosis of Rotating Machinery With Multiple New Faults
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot
The Graph Neural Network Model
inferredcites →https://doi.org/10.1109/tnnls.2015.2424995openalex-snapshot
Extreme Learning Machine for Multilayer Perceptron
inferredcites →https://doi.org/10.1126/science.290.5500.2319openalex-snapshot
A Global Geometric Framework for Nonlinear Dimensionality Reduction
inferredcites →https://doi.org/10.1126/science.290.5500.2323openalex-snapshot
Nonlinear Dimensionality Reduction by Locally Linear Embedding
inferredcites →https://doi.org/10.1145/2939672.2939753openalex-snapshot
Structural Deep Network Embedding
inferredcites →https://doi.org/10.1177/1077546314547533openalex-snapshot
Rolling bearing fault diagnosis approach using probabilistic principal component analysis denoising and cyclic bispectrum
inferredcites →https://doi.org/10.1177/1475921719897317openalex-snapshot
A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery
inferredcites →https://doi.org/10.1609/aaai.v30i1.10179openalex-snapshot
Deep Neural Networks for Learning Graph Representations
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.3997545openalex-extracted
A Stacked Sparse Autoencoder Embedded Subspace Correlation Alignment Application to Fault Diagnosis of Rolling Bearings
inferred← citeshttps://doi.org/10.1016/j.aei.2024.102432openalex-snapshot
Federated temporal-context contrastive learning for fault diagnosis using multiple datasets with insufficient labels
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109271openalex-snapshot
Deep imbalanced regression using cost-sensitive learning and deep feature transfer for bearing remaining useful life estimation
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inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110689openalex-snapshot
Dimensionality reduction of rolling bearing fault data based on graph-embedded semi-supervised deep auto-encoders
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128615openalex-snapshot
Graph isomorphism wavelet convolutional networks for small-sample fault diagnosis of rotating machinery using multi-sensor information fusion
inferred← citeshttps://doi.org/10.1016/j.jprocont.2023.102999openalex-snapshot
Stacked supervised auto-encoder with graph regularization for feature extraction and fault classification in chemical processes
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.110172openalex-snapshot
Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis
inferred← citeshttps://doi.org/10.1016/j.measurement.2022.111697openalex-snapshot
Motor current signal analysis using hypergraph neural networks for fault diagnosis of electromechanical system
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inferred← citeshttps://doi.org/10.1016/j.ymssp.2021.108616openalex-snapshot
A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings
inferred← citeshttps://doi.org/10.1016/j.ymssp.2022.109615openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ymssp.2023.110491openalex-snapshot
Prior knowledge-embedded meta-transfer learning for few-shot fault diagnosis under variable operating conditions
inferred← citeshttps://doi.org/10.1063/5.0130984openalex-snapshot
An improved re-parameterized visual geometry group network for rolling bearing fault diagnosis
inferred← citeshttps://doi.org/10.1063/5.0171091openalex-snapshot
IRMSwin-T: A lightweight shifted windows transformer based on inverted residual structure and residual multi-layer perceptron for rolling bearing fault diagnosis
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A novel self-training semi-supervised deep learning approach for machinery fault diagnosis
inferred← citeshttps://doi.org/10.1088/1361-6501/aca348openalex-snapshot
Remaining useful life prediction for rotating machinery based on dynamic graph and spatial–temporal network
inferred← citeshttps://doi.org/10.1088/1361-6501/ad5ea4openalex-snapshot
Lightweight bearing fault diagnosis method based on cross-scale learning transformer under imbalanced data*
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Fault diagnosis method for voltage transformers based on wavelet threshold filtering and graph neural networks
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Intelligent Cross-domain Fault Diagnosis For Rotating Machinery Using Multiscale Adversarial Convolutional Neural Network
inferred← citeshttps://doi.org/10.1109/icaic57335.2023.10044173openalex-snapshot
Bearing Fault Diagnosis Based on Sparse Wavelet Decomposition and Sparse Graph Connection Using GraphSAGE
inferred← citeshttps://doi.org/10.1109/icmsp58539.2023.10170907openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/itme56794.2022.00108openalex-snapshot
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Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction
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CapsFormer: A Novel Bearing Intelligent Fault Diagnosis Framework With Negligible Speed Change Under Small-Sample Conditions
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Unsupervised Deep Learning for Anomaly Detection in Automotive Trucks: A Survey
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Multiscale Time-Frequency Sparse Transformer Based on Partly Interpretable Method for Bearing Fault Diagnosis
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NGO-MRE combined with DELM for bearing fault diagnosis
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A Multi-Information Fusion ViT Model and Its Application to the Fault Diagnosis of Bearing with Small Data Samples
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An Industrial Fault Diagnostic System Based on a Cubic Dynamic Uncertain Causality Graph
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Intelligent Diagnosis of Rolling Bearings Fault Based on Multisignal Fusion and MTF-ResNet
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