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Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model.

, , , , and . Reliab. Eng. Syst. Saf., (2019)

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Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery., , , and . IEEE Trans. Ind. Electron., 68 (8): 7496-7504 (2021)An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions., , , , and . IEEE Trans. Ind. Informatics, 20 (4): 6177-6186 (April 2024)A dirty data recognition method for machinery condition monitoring in big data era., , , and . IECON, page 7061-7066. IEEE, (2017)Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model., , , , and . Reliab. Eng. Syst. Saf., (2019)A Robust Wavelet-integrated Residual Network for Fault Diagnosis of Machines with Adversarial Training., , , and . AIM, page 847-851. IEEE, (2023)Accurate identification of Parkinson's disease by distinctive features and ensemble decision trees., , , , , and . Biomed. Signal Process. Control., (2021)A method of automatic feature extraction from massive vibration signals of machines., , , and . I2MTC, page 1-6. IEEE, (2016)Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process., , and . ICPHM, page 1-6. IEEE, (2018)A multimodal dynamic parameterized bilinear factorized framework for remaining useful life prediction under variational data., , , , , , and . Reliab. Eng. Syst. Saf., (2024)Non-Contact Machine Vibration Sensing and Fault Diagnosis Method Based on Event Camera., , , , and . SAFEPROCESS, page 1-5. IEEE, (2023)