基于截断p-shrinkage范数的航空发动机数据重构

Aero-engine data reconstruction based on truncated p-shrinkage norm

针对航空发动机传感器的数据缺失问题,提出基于张量奇异值阈值(TSVT)的张量重构模型LRTC-PTNN,对航空发动机的传感器数据进行重构。LRTC-PTNN模型运用截断pshrinkage范数的方式代替原始张量迹范数作为张量秩的凸包络,并根据TSVT的特性,计算了传感器之间的相关性,选取传感器截面作为重构精度最佳的数据输入方向,使用交替乘子法实现LRTCPTNN算法。选取NASA提供的PHM2008数据集进行实验,对数据集进行标准化,并在重构后进行恢复,将多个时间序列个数相近的发动机传感器数据构建为高维张量的形式,设置2种传感器的数据缺失场景进行实验,结果表明:重构后数据的均方根误差和平均绝对百分比误差范围分别为2.10~13.13和0.32~1.49,LRTC-PTNN模型优于现有的基线模型,且在极端情况下有较强的鲁棒性。

To address the data loss problem of aero engine sensors,a tensor reconstruction model LRTC-PTNN based on tensor singular value threshold(TSVT)is proposed to reconstruct the sensor data of aircraft engines.LRTCPTNN uses truncation p-shrinkage norm to replace the original tensor trace norm as the convex envelope of tensor rank.According to the characteristics of TSVT,the correlation between sensors is calculated,and the data input direction with the best reconstruction accuracy is selected.The LRTC-PTNN algorithm was finally implemented using the alternating direction method of multipliers.Using the PHM2008 dataset provided by NASA for experiments,the dataset was standardized and restored after reconstruction,and the multiple time series similar number of engine sensor data were constructed into the form of high-dimensional tensor,and the data deletion scenarios of the two sensors were set for experiments.The results showed that the RMSE and MAPE values of the reconstructed data were between 2.10%−13.13%and 0.32%−1.49%,respectively;the LRTC-PTNN model was better than the existing baseline model;in extreme cases,the model also has strong robustness.