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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

135 related items for PubMed ID: 33985788

  • 1.
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  • 2. An effective method for remaining useful life estimation of bearings with elbow point detection and adaptive regression models.
    Yan M, Xie L, Muhammad I, Yang X, Liu Y.
    ISA Trans; 2022 Sep; 128(Pt A):290-300. PubMed ID: 34799099
    [Abstract] [Full Text] [Related]

  • 3. Joint Learning of Failure Mode Recognition and Prognostics for Degradation Processes.
    Wang D, Xian X, Song C.
    IEEE Trans Autom Sci Eng; 2024 Apr; 21(2):1421-1433. PubMed ID: 38595999
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  • 5. Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model.
    Yan M, Wang X, Wang B, Chang M, Muhammad I.
    ISA Trans; 2020 Mar; 98():471-482. PubMed ID: 31492470
    [Abstract] [Full Text] [Related]

  • 6. The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder.
    Gao Y, Ahmad Z, Kim JM.
    Sensors (Basel); 2024 Apr 09; 24(8):. PubMed ID: 38675999
    [Abstract] [Full Text] [Related]

  • 7. Deep learning-based anomaly-onset aware remaining useful life estimation of bearings.
    Kamat PV, Sugandhi R, Kumar S.
    PeerJ Comput Sci; 2021 Apr 09; 7():e795. PubMed ID: 34909464
    [Abstract] [Full Text] [Related]

  • 8. Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery.
    Ma M, Sun C, Mao Z, Chen X.
    ISA Trans; 2020 Oct 09. PubMed ID: 34756307
    [Abstract] [Full Text] [Related]

  • 9. A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM.
    Huang G, Li H, Ou J, Zhang Y, Zhang M.
    Sensors (Basel); 2020 Mar 27; 20(7):. PubMed ID: 32230874
    [Abstract] [Full Text] [Related]

  • 10. Joint optimization of degradation assessment and remaining useful life prediction for bearings with temporal convolutional auto-encoder.
    Ding Y, Jia M, Zhao X, Yan X, Lee CG.
    ISA Trans; 2024 Mar 27; 146():451-462. PubMed ID: 38320915
    [Abstract] [Full Text] [Related]

  • 11. Performance Degradation Prediction Using LSTM with Optimized Parameters.
    Hu Y, Wei R, Yang Y, Li X, Huang Z, Liu Y, He C, Lu H.
    Sensors (Basel); 2022 Mar 21; 22(6):. PubMed ID: 35336579
    [Abstract] [Full Text] [Related]

  • 12. Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM.
    Wang H, Zhang X, Ren M, Xu T, Lu C, Zhao Z.
    Entropy (Basel); 2023 Oct 25; 25(11):. PubMed ID: 37998169
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  • 14. Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification.
    Yang J, Peng Y, Xie J, Wang P.
    Sensors (Basel); 2022 Jun 16; 22(12):. PubMed ID: 35746338
    [Abstract] [Full Text] [Related]

  • 15. Machinery Prognostics and High-Dimensional Data Feature Extraction Based on a Transformer Self-Attention Transfer Network.
    Sun S, Peng T, Huang H.
    Sensors (Basel); 2023 Nov 15; 23(22):. PubMed ID: 38005579
    [Abstract] [Full Text] [Related]

  • 16. A Cotraining-Based Semisupervised Approach for Remaining-Useful-Life Prediction of Bearings.
    Yan X, Xia X, Wang L, Zhang Z.
    Sensors (Basel); 2022 Oct 13; 22(20):. PubMed ID: 36298116
    [Abstract] [Full Text] [Related]

  • 17. Remaining Useful Life prediction of rolling bearings based on risk assessment and degradation state coefficient.
    Li Q, Yan C, Chen G, Wang H, Li H, Wu L.
    ISA Trans; 2022 Oct 13; 129(Pt B):413-428. PubMed ID: 35181005
    [Abstract] [Full Text] [Related]

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  • 19. A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction.
    Zhao C, Huang X, Li Y, Yousaf Iqbal M.
    Sensors (Basel); 2020 Dec 11; 20(24):. PubMed ID: 33322457
    [Abstract] [Full Text] [Related]

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