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Order-Aware and Speed-Disentangled Subspace Learning for Cross-Speed Bearing Misalignment Diagnosis
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inferredcites →https://doi.org/10.1006/mssp.1996.0056openalex-snapshot
ANALYSIS OF COMPUTED ORDER TRACKING
inferredcites →https://doi.org/10.1007/s00202-021-01309-2openalex-snapshot
Enhanced bearing fault detection using multichannel, multilevel 1D CNN classifier
inferredcites →https://doi.org/10.1007/s10994-009-5152-4openalex-snapshot
A theory of learning from different domains
inferredcites →https://doi.org/10.1016/j.asoc.2021.107702openalex-snapshot
OrbitNet: A new CNN model for automatic fault diagnostics of turbomachines
inferredcites →https://doi.org/10.1016/j.compind.2018.12.012openalex-snapshot
Ensemble deep learning-based fault diagnosis of rotor bearing systems
inferredcites →https://doi.org/10.1016/j.egyr.2022.08.041openalex-snapshot
Bearing fault diagnosis based on speed signal and CNN model
inferredcites →https://doi.org/10.1016/j.engappai.2026.115106openalex-snapshot
A single domain generalization fault diagnosis method based on multi-scale style enhancement and causal contribution alignment
inferredcites →https://doi.org/10.1016/j.eswa.2011.02.074openalex-snapshot
Rotor fault diagnosis system based on sGA-based individual neural networks
inferredcites →https://doi.org/10.1016/j.eswa.2018.05.032openalex-snapshot
A new subset based deep feature learning method for intelligent fault diagnosis of bearing
inferredcites →https://doi.org/10.1016/j.eswa.2024.125137openalex-snapshot
Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network
inferredcites →https://doi.org/10.1016/j.eswa.2025.127513openalex-snapshot
A progressive self-supervised learning framework: From fault diagnosis to electric motor wear prediction
inferredcites →https://doi.org/10.1016/j.isatra.2014.06.002openalex-snapshot
Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis
inferredcites →https://doi.org/10.1016/j.neucom.2018.06.078openalex-snapshot
A survey on Deep Learning based bearing fault diagnosis
inferredcites →https://doi.org/10.1016/s0022-0000(03)00025-4openalex-snapshot
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
inferredcites →https://doi.org/10.1080/10589759.2023.2228979openalex-snapshot
Empirical-based DA and ANN to diagnose misalignment in rotor-bearing system
inferredcites →https://doi.org/10.1090/conm/026/737400openalex-snapshot
Extensions of Lipschitz mappings into a Hilbert space
inferredcites →https://doi.org/10.1109/41.873206openalex-snapshot
A review of induction motors signature analysis as a medium for faults detection
inferredcites →https://doi.org/10.1109/79.221324openalex-snapshot
Signal processing with higher-order spectra
inferredcites →https://doi.org/10.1109/msp.2007.914731openalex-snapshot
An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition]
inferredcites →https://doi.org/10.1109/tie.2017.2774777openalex-snapshot
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
inferredcites →https://doi.org/10.1109/tie.2019.2942548openalex-snapshot
Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization
inferredcites →https://doi.org/10.1109/tii.2019.2955540openalex-snapshot
Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis
inferredcites →https://doi.org/10.1109/tim.2023.3238032openalex-snapshot
CFFsBD: A Candidate Fault Frequencies-Based Blind Deconvolution for Rolling Element Bearings Fault Feature Enhancement
inferredcites →https://doi.org/10.1109/tim.2023.3312761openalex-snapshot
A Comparative Experimental Study of Vibration and Acoustic Emission on Fault Diagnosis of Low-Speed Bearing
inferredcites →https://doi.org/10.1109/tpami.2013.50openalex-snapshot
Representation Learning: A Review and New Perspectives
inferredcites →https://doi.org/10.1109/tsmc.2020.3048950openalex-snapshot
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
inferredcites →https://doi.org/10.1177/1077546320929830openalex-snapshot
Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features
inferredcites →https://doi.org/10.1364/oe.480816openalex-snapshot
A visual defect detection for optics lens based on the YOLOv5 -C3CA-SPPF network model
inferredcites →https://doi.org/10.1609/aaai.v36i8.20881openalex-snapshot
TS2Vec: Towards Universal Representation of Time Series
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