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Resource-efficient class-balanced Bagging framework for rolling bearing fault diagnosis

https://doi.org/10.2139/ssrn.6508399
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11071-024-10157-1openalex-snapshot
A performance-interpretable intelligent fusion of sound and vibration signals for bearing fault diagnosis via dynamic CAME
inferredcites →https://doi.org/10.1007/s42417-022-00605-wopenalex-snapshot
A Deep Intelligent Hybrid Model for Fault Diagnosis of Rolling Bearing
inferredcites →https://doi.org/10.1016/j.aej.2024.09.052openalex-snapshot
Integrated development of the industrial chain and innovation chain of high-tech manufacturing industry based on the Internet of Things
inferredcites →https://doi.org/10.1016/j.eswa.2025.128592openalex-snapshot
DTMPI-DIVR: A digital twins for multi-margin physical information via dynamic interaction of virtual and real sound-vibration signals for bearing fault diagnosis without real fault samples
inferredcites →https://doi.org/10.1016/j.jss.2017.07.006openalex-snapshot
Adaptive Ensemble Undersampling-Boost: A novel learning framework for imbalanced data
inferredcites →https://doi.org/10.1016/j.knosys.2019.07.008openalex-snapshot
Deep learning fault diagnosis method based on global optimization GAN for unbalanced data
inferredcites →https://doi.org/10.1016/j.measurement.2020.107567openalex-snapshot
A novel class imbalance-robust network for bearing fault diagnosis utilizing raw vibration signals
inferredcites →https://doi.org/10.1016/j.measurement.2025.117057openalex-snapshot
Parity equation model-based fault diagnosis method newly applied to bearing faults in induction motors
inferredcites →https://doi.org/10.1016/j.measurement.2025.118661openalex-snapshot
Enhancing machine learning classifier performance in bearing fault diagnosis through feature-based ranking
inferredcites →https://doi.org/10.1016/j.rineng.2024.102700openalex-snapshot
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
inferredcites →https://doi.org/10.1016/j.ymssp.2015.04.021openalex-snapshot
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
inferredcites →https://doi.org/10.1016/j.ymssp.2022.109175openalex-snapshot
Vibration feature extraction using signal processing techniques for structural health monitoring: A review
inferredcites →https://doi.org/10.1016/j.ymssp.2022.109454openalex-snapshot
Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1023/a:1018054314350openalex-snapshot
Bagging Predictors
inferredcites →https://doi.org/10.1093/jcde/qwad081openalex-snapshot
A diagnosis method for imbalanced bearing data based on improved SMOTE model combined with CNN-AM
inferredcites →https://doi.org/10.1093/jcde/qwae075openalex-snapshot
Review of imbalanced fault diagnosis technology based on generative adversarial networks
inferredcites →https://doi.org/10.1109/sdpc.2018.8665006openalex-snapshot
Health Assessment for Piston Pump Using LSTM Neural Network
inferredcites →https://doi.org/10.1109/tim.2023.3246470openalex-snapshot
A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis
inferredcites →https://doi.org/10.1109/tnnls.2020.2964585openalex-snapshot
Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems
inferredcites →https://doi.org/10.1201/9781003587774openalex-snapshot
Ensemble Methods
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.3926/jiem.3597openalex-snapshot
Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey
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