Aeroengine sensor data reconstruction with missing data
ZHOU Yuan1,2, ZUO Hongfu2, HE Jun1
1. College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Abstract：Aiming at handling incomplete sensor data, we propose an online-reconstruction model based on the polar incremental matrix completion (PIMC) algorithm for aeroengine sensor data, which can represent the evolving features of system by subspace. The model extracts the current data feature from the history data and updates the subspace to track the evolving features via new data. The proposed model was validated and compared on two simulated datasets and the normalized mean square errors (MSE) between the reconstruction by PIMC and the ground truth are all less than 1×10-5. The experimental results show that the proposed model is practical for aeroengine sensor data reconstruction, which is robust to missing data and noise.
周媛, 左洪福, 何军. 信息缺失的航空发动机传感器数据重构[J]. 北京航空航天大学学报, 2016, 42(5): 891-898.
ZHOU Yuan, ZUO Hongfu, HE Jun. Aeroengine sensor data reconstruction with missing data. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2016, 42(5): 891-898.
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2016, Vol. 42 
