Prediction on Residual Life of Civil Aviation Engine under Imperfect Maintenance
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摘要: 针对现阶段剩余寿命(RUL)预测方法没有考虑在发动机性能衰退阶段维修因素影响的问题,提出了以考虑非完美维修下的性能衰退模型预测民航发动机RUL的方法。采用带漂移点的Wiener过程对民航发动机的性能退化进行建模。根据历史性能退化数据以及历史维修记录数据,通过极大似然估计算法对模型参数进行估计,实现对航空发动机的RUL预测。通过航空公司实际发动机机载快速存取记录器(QAR)数据进行模型验证,结果表明:该方法能够更好地跟踪发动机实际性能退化过程,预测精度较高,能为民航发动机维修计划的制定提供依据。Abstract: In view of the problem that the current Remaining Useful Life (RUL) prediction method does not consider the influence of maintenance factors in the engine performance degradation stage, a new method for predicting the civil aviation engine' s RUL by considering the performance degradation model under imperfect maintenance is proposed. The performance degradation of civil aviation engines was modeled using a Wiener process with drift points. According to the historical performance degradation data and the historical maintenance record data, the model parameters are estimated by the maximum likelihood estimation algorithm to realize the RUL prediction of the aeroengine. The actual monitoring data of the engine is used to verify the prediction results. The results show that the method can better track the actual performance degradation process of the engine, and the prediction accuracy is higher, which can provide a basis for the formulation of the civil aviation engine maintenance plan analysis.
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Key words:
- imperfect maintenance /
- residual life prediction /
- Wiener process /
- aircraft engines
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表 1 维修后发动机EGTM变化表
NUM ESN EGTM_B/℃ EGTM_A/℃ △EGTM/℃ 1 4***1 25.76 33.22 7.46 2 4***1 15.86 30.58 14.72 3 4***2 37.10 42.27 5.17 4 4***2 34.47 39.54 5.07 $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ 56 4**29 16.63 22.25 5.62 57 4**30 26.49 34.02 4.53 表 2 基准模型与改进模型参数估计值
ESN ${\hat \mu _{\theta st}}$ ${\hat \sigma _{\theta st}}$ ${\sigma _{{{st}}}}$ ${\hat \mu _\theta }$ ${\hat \sigma _\theta }$ $\hat \sigma $ 4***1 0.182 0.029 0.024 0.324 0.028 0.022 4***2 0.177 0.034 0.032 0.341 0.026 0.028 $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ 4***9 0.201 0.042 0.022 0.285 0.027 0.023 4**10 0.199 0.030 0.024 0.318 0.021 0.024 -
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