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Self-supervised learning-based dual-classifier domain adaptation model for rolling bearings cross-domain fault diagnosis.

, , , , , and . Knowl. Based Syst., (2024)

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RUL prediction for rolling bearings based on Convolutional Autoencoder and status degradation model., , , , and . Appl. Soft Comput., (2022)A neighborhood parameter optimization method of LLE based on topology preservation., , and . EMEIT, page 4231-4234. IEEE, (2011)Self-supervised learning-based dual-classifier domain adaptation model for rolling bearings cross-domain fault diagnosis., , , , , and . Knowl. Based Syst., (2024)New RUL Prediction Method for Rotating Machinery via Data Feature Distribution and Spatial Attention Residual Network., , , , and . IEEE Trans. Instrum. Meas., (2023)Dynamic obstacle-surmounting analysis of a bilateral-wheeled cable-climbing robot for cable-stayed bridges., and . Ind. Robot, 46 (3): 431-443 (2019)Res-HSA: Residual hybrid network with self-attention mechanism for RUL prediction of rotating machinery., , , , and . Eng. Appl. Artif. Intell., (2023)Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network., , , , and . Eng. Appl. Artif. Intell., (May 2023)Supervised Laplacian Eigenmaps for Machinery Fault Classification., and . CSIE (7), page 116-120. IEEE Computer Society, (2009)A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions., , , , , and . Appl. Soft Comput., (October 2023)New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network., , , and . Sensors, 18 (2): 337 (2018)