Fault prediction model for rolling bearings based on double adaptive sliding time windows

Baoshan Zhang; Jilian Guo; Mingliang Zhang; Zhangwen Zhou; Xuan Wang; Ziyi Guo

doi:10.1117/12.3014773

4 March 2024 Fault prediction model for rolling bearings based on double adaptive sliding time windows

Baoshan Zhang, Jilian Guo, Mingliang Zhang, Zhangwen Zhou, Xuan Wang, Ziyi Guo

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129813Y (2024) https://doi.org/10.1117/12.3014773
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

To address the problems of traditional and neural network-based methods in rolling bearing fault prediction, we proposed a fault prediction model for rolling bearings based on double adaptive sliding time windows. Firstly, the rolling bearing vibration signal is mapped into fault features that can characterise its degradation state by setting up a state estimation non-linear operator that can remove correlations. Secondly, a loss function is used as a criterion to set up an adaptive update mechanism for the model parameters, as well as a sliding time window capable of adaptively selecting the data length. Finally, the validity of the proposed failure prediction model is verified by simulating the occurrence of failures under the combined sudden and gradual failures in practice using the whole life cycle data of rolling bearings released by Xi'an Jiaotong University.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Baoshan Zhang, Jilian Guo, Mingliang Zhang, Zhangwen Zhou, Xuan Wang, and Ziyi Guo "Fault prediction model for rolling bearings based on double adaptive sliding time windows", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129813Y (4 March 2024); https://doi.org/10.1117/12.3014773

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