| Citation: | ZHANG H M，WU J N，ZHAO Y M，et al. Aero-engine data reconstruction based on truncated p-shrinkage norm[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（1）：39-47 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0263
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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. LRTC-PTNN 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.
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