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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (3): 225064-225064.doi: 10.7527/S1000-6893.2021.25064

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Cumulative damage index model and service reliability evaluation of turbine blade

LEI Shiying, SUN Jianzhong, LIU He   

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106
  • Received:2020-12-07 Revised:2020-12-28 Online:2022-03-15 Published:2021-01-21
  • Supported by:
    National Natural Science Foundation of China (91860139, 52072176)

Abstract: High Pressure Turbine (HPT) blades are one of the key structural parts of civil aviation engines, directly related to the performance, reliability and service life of the engine.This paper proposes a service reliability assessment method for HPT blades.The blade creep damage is evaluated based on historical flight condition data under service conditions, combined with the engine performance model, the stress and temperature calculation model of the key point, and the creep damage assessment model.After calculation, and in consideration of the multi-modal data under service conditions, a cumulative damage index model is established for creep failure, and the service reliability of the blade is evaluated by fusing the historical covariate information.The simulation results show that the creep cumulative damage index defined in the article can make full use of the historical usage information, state parameters and truncated failure data of the engine under service conditions to realize the service reliability assessment and remaining life prediction of the turbine blades under specific service conditions.Compared with traditional reliability analysis methods, the cumulative damage index prediction model can provide more reliable evaluation results based on the service conditions of a single aircraft and better support for aero engine operation risk assessment and condition-based maintenance decision-making.

Key words: turbine blades, creep life, service reliability, cumulative damage index model, model and data fusion

CLC Number: