| Citation: | LIU Junqiang, HU Dongbin, PAN Chunlu, et al. Remaining useful life prediction of multi-stage aero-engine based on super statistics[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 56-64. doi: 10.13700/j.bh.1001-5965.2019.0647(in Chinese)
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Traditional aero-engine Remaining Useful Life (RUL) model cannot objectively describe the multi-stage degeneration process, and the accuracy of RUL prediction is low. To solve this problem, a new multi-stage RUL prediction model for RUL prediction is proposed, including super statistics theory, mutation point detection, Unscented Kalman Filter (UKF) and nonlinear prediction. In the paper, a Multi-stage Segmentation Filtering based on Super statistics (BS-MSF) theory algorithm is proposed. In this algorithm, first, super statistics theory is used to conduct mutation point detection and divide the health data of aero-engine into multiple degradation phases. Then, UKF is used to filter the fused time-varying parameters. Finally, the real RUL of the aero-engine is estimated by nonlinear fitting. nonlinear fitting, and the aero-engine data was released by National Aeronautics and Space Administration. Simulation results show that the presented method has better adaptability in predicting engine performance degradation, smaller fitting error, and more accurate prediction of RUL. The prediction accuracy is 5.5% higher than that of single-stage method.
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