TY - GEN
T1 - Multi-Sensor Infusion and Data-Physics Model Based Remaining Life Prediction
AU - Xiao, Xiaoqi
AU - Xu, Dan
AU - Feng, Zhixin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - The electromechanical system has high-dimensional sensor information, which often suffers multiple working conditions. Therefore, it is necessary and challenging to study multi-sensor fusion technology under variable loads. In this paper, we proposed a method for predicting remaining useful life under time-varying working load based on multi-sensor fusion and data-physical models. First of all, we established the main sensor selection model through principal component analysis. Then, the multi-sensor information fusion model under variable load was obtained by combining with the constructed load, sensor data and the location scale model of the failure time. Finally, the C-MAPSS dataset was used to verify the effectiveness of the proposed method.
AB - The electromechanical system has high-dimensional sensor information, which often suffers multiple working conditions. Therefore, it is necessary and challenging to study multi-sensor fusion technology under variable loads. In this paper, we proposed a method for predicting remaining useful life under time-varying working load based on multi-sensor fusion and data-physical models. First of all, we established the main sensor selection model through principal component analysis. Then, the multi-sensor information fusion model under variable load was obtained by combining with the constructed load, sensor data and the location scale model of the failure time. Finally, the C-MAPSS dataset was used to verify the effectiveness of the proposed method.
KW - Data-physics model
KW - Multi-sensor fusion
KW - Principle Component Analysis
KW - Remaining useful life prediction
KW - Timevarying working load
UR - https://www.scopus.com/pages/publications/85099279468
U2 - 10.1109/PHM-Jinan48558.2020.00077
DO - 10.1109/PHM-Jinan48558.2020.00077
M3 - 会议稿件
AN - SCOPUS:85099279468
T3 - Proceedings - 11th International Conference on Prognostics and System Health Management, PHM-Jinan 2020
SP - 395
EP - 400
BT - Proceedings - 11th International Conference on Prognostics and System Health Management, PHM-Jinan 2020
A2 - Li, Chuan
A2 - Gjorgjevikj, Dejan
A2 - Yang, Zhe
A2 - Pu, Ziqiang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Prognostics and System Health Management, PHM-Jinan 2020
Y2 - 23 October 2020 through 25 October 2020
ER -