美国《工程索引》核心期刊
中国科学引文数据库核心期刊
国际刊号:ISSN 1005-1120
国内刊号:CN 32-1389/V
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  • 主管:工业和信息化部
  • 主办:南京航空航天大学
  • 国际刊号:ISSN 1005-1120
  • 国内刊号:CN 32-1389/V
  • 地址:南京市御道街29号
  • 电话:025-84892071
  • 传真:025-84892071
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  • 邮编:210016
Cai Jing, Zhang Li, Dong Ping.Remaining Useful Life Prediction for Aero Engines Combining Sate Space Model and KF Algorithm[J].南京航空航天大学学报英文版,2017,34(3):265-271
Remaining Useful Life Prediction for Aero Engines Combining Sate Space Model and KF Algorithm
  
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中文关键词:  
英文关键词:remaining useful life; exhaust gas temperature margin (EGTM); Kalman filter; Sate space model
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作者单位
Cai Jing, Zhang Li, Dong Ping College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P.R. China 
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英文摘要:
      The key to failure prevention for aero engine lies in performance prediction and the exhaust gas temperature margin (EGTM) is used as the most important degradation parameter to obtain the operating performance of the aero engine. Because of the complex environment interference, EGTM always has strong randomness, and the state space based degradation model can identify the noisy observation from the true degradation state, which is more close to the actual situations. Therefore, a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life (RUL). As one of the most effective methods for both linear state estimation and parameter estimation, Kalman filter (KF) is applied. Firstly, with EGTM degradation data, state space model approach is used to set up a state space model for aero engine. Secondly, RUL of aero engine is analyzed, and expected RUL and distribution of RUL are determined. Finally, the sate space model and KF algorithm are applied to an example of CFM 56 aero engine. The expected RUL is predicted, and corresponding probability density distribution (PDF) and cumulative distribution function (CDF) are given. The result indicates that the accuracy of RUL prediction reaches 7.76% ahead 580 flight cycles (FC), which is more accurate than linear regression, and therefore shows the validity and rationality of the proposed method.
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