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Machinery health indicator construction based on convolutional neural networks considering trend burr.

, , , , и . Neurocomputing, (2018)

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Li Li

Li Li

Li Li

Li Li

Li Li

 

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Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model., , , , и . Reliab. Eng. Syst. Saf., (2019)Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery., , , , и . Reliab. Eng. Syst. Saf., (2023)A multimodal dynamic parameterized bilinear factorized framework for remaining useful life prediction under variational data., , , , , , и . Reliab. Eng. Syst. Saf., (2024)Non-Contact Machine Vibration Sensing and Fault Diagnosis Method Based on Event Camera., , , , и . SAFEPROCESS, стр. 1-5. IEEE, (2023)Machinery Health Prognostics With Multimodel Fusion Degradation Modeling., , , , и . IEEE Trans. Ind. Electron., 70 (11): 11764-11773 (ноября 2023)Degradation modeling and remaining useful life prediction for dependent competing failure processes., , , , и . Reliab. Eng. Syst. Saf., (2021)Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions., , , , , и . Reliab. Eng. Syst. Saf., (2020)Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process., , и . ICPHM, стр. 1-6. IEEE, (2018)Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data., , , , и . IEEE Trans. Ind. Electron., 66 (9): 7316-7325 (2019)Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings., , , и . IEEE Trans. Ind. Electron., 69 (8): 8430-8439 (2022)