The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate r...The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.展开更多
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 a...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-56aero-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.展开更多
排气温度是表征发动机工作状态的主要参数之一,通过对多个飞行架次的排气温度裕度(Exhaust Gas Temperature Margin,EGTM)进行预测分析,能够在一定程度上反映发动机工作性能,为后续故障检测工作提供理论依据。针对EGTM数据的非线性、非...排气温度是表征发动机工作状态的主要参数之一,通过对多个飞行架次的排气温度裕度(Exhaust Gas Temperature Margin,EGTM)进行预测分析,能够在一定程度上反映发动机工作性能,为后续故障检测工作提供理论依据。针对EGTM数据的非线性、非平稳特征,提出了基于粒子群优化算法(Particle Swarm Optimization,PSO)的极限学习机(Extreme Learning Machine,ELM)预测方法。通过ELM构建EGTM的预测模型,并利用PSO算法对其参数进行优化以保证模型的精确性;以某航空发动机EGTM数据作为验证,结果表明,相比于传统的预测方法,RMSE与MAE分别降低至1.889 8、1.0,有效提高了预测精度。展开更多
基金supported by the National Natural Science Foundation of China(No.1733201)。
文摘The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.
文摘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-56aero-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.
文摘排气温度是表征发动机工作状态的主要参数之一,通过对多个飞行架次的排气温度裕度(Exhaust Gas Temperature Margin,EGTM)进行预测分析,能够在一定程度上反映发动机工作性能,为后续故障检测工作提供理论依据。针对EGTM数据的非线性、非平稳特征,提出了基于粒子群优化算法(Particle Swarm Optimization,PSO)的极限学习机(Extreme Learning Machine,ELM)预测方法。通过ELM构建EGTM的预测模型,并利用PSO算法对其参数进行优化以保证模型的精确性;以某航空发动机EGTM数据作为验证,结果表明,相比于传统的预测方法,RMSE与MAE分别降低至1.889 8、1.0,有效提高了预测精度。
