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Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions., , , , , и . Reliab. Eng. Syst. Saf., (2023)Intelligent machinery health prognostics under variable operation conditions with limited and variable-length data., , , , и . Adv. Eng. Informatics, (2022)Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets., , , , , , , , и . ICDM, стр. 1317-1322. IEEE Computer Society, (2016)Statistical Alignment-Based Metagated Recurrent Unit for Cross-Domain Machinery Degradation Trend Prognostics Using Limited Data., , , и . IEEE Trans. Instrum. Meas., (2021)Remaining Useful Life Estimation Under Multiple Operating Conditions via Deep Subdomain Adaptation., , и . IEEE Trans. Instrum. Meas., (2021)Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment., , , , , и . IEEE Trans. Ind. Informatics, 20 (2): 1313-1323 (февраля 2024)A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings., , , и . Reliab. Eng. Syst. Saf., (2021)Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings., , , и . Reliab. Eng. Syst. Saf., 218 (Part): 108126 (2022)A novel Time-frequency Transformer and its Application in Fault Diagnosis of Rolling Bearings., , , и . CoRR, (2021)Deep imbalanced regression using cost-sensitive learning and deep feature transfer for bearing remaining useful life estimation., , , и . Appl. Soft Comput., (2022)