Abstract:

Aiming at the problem of poor optimization accuracy of the aero-engine health factor， an aero-engine performance degradation evaluation method based on the improved L-SHADE algorithm is proposed. Firstly， the fitness function of engine health factor estimation is expanded by the multi-operating point analysis method to solve the problem of insufficient number of gas path measurement sensors. Secondly， the problem of rapid population reduction is solved by introducing a nonlinear population reduction strategy. Meanwhile， through the improved optimized weighted mutation strategy， the weights of the greedy operators in different iteration stages are changed， which increases the global search and local development capabilities of the algorithm. Finally， the convergence accuracy and robustness of the improved algorithm are verified on 30 classic benchmark functions. The calculation results of the actual aero-engine performance degradation evaluation show that the improved L-SHADE algorithm enhances the population diversity in the early stage of the algorithm iteration and the development ability in the later stage of the algorithm. Compared with that of the standard L-SHADE algorithm， the calculation accuracy of the improved algorithm is improved by 65.5% on average. The calculation results satisfy the requirements of engineering accuracy with strong engineering adaptability. It can be applied to the flight parameter data， providing a theoretical basis for engine health management and performance monitoring.

Key words: performance degradation, health factor, population reduction, weighted mutation, performance monitoring