A Hybrid Degradation Evaluation Model for Aero-Engines

Abstract

The non-convergence and low efficiency of the thermodynamic model make them difficult to be used in the aero-engines degradation evaluation, while the negligence of the thermodynamics process of data-driven degradation evaluation methods makes them inaccurate and hard to analyze the actual degradation of air path components. So, we propose a thermodynamic-based and data-driven hybrid model for aero-engine degradation evaluation. Different from thermodynamic-based methods, the iteration calculation is converted to the forward flow in the proposed neural network, thus improving convergence. Moreover, a multi-objective loss function considering the components co-operation process and fusion training process fully taking advantage of simulation and degradation trajectory datasets are proposed to improve the degradation evaluation accuracy. The test case is carried out on NASA’s benchmark for aero-engine degradation evaluation. The result shows that the proposed method can improve the accuracy significantly, which suggests its effectiveness.

Parallel processing of sensor signals using deep learning method for aero-engine remaining useful life prediction

“…Chao et al [19] proposed a data-driven model with physical enhancements that can be used for RUL prediction of aero-engines by combining the information of a physically based physical performance model with a deep learning algorithm. Ren et al [20] proposed a hybrid approach based on thermodynamic modeling and data-driven approach for performance degradation assessment of aero-engines. Hybrid prediction methods can synthesize the advantages of multiple methods that have certain development potential.…”