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
对锂离子电池的荷电状态(state of charge,SOC),健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)进行准确估计是锂离子电池安全稳定运行的重要保障,该文提出一种结合充电电压片段和等效电路模型(equivalent ci...对锂离子电池的荷电状态(state of charge,SOC),健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)进行准确估计是锂离子电池安全稳定运行的重要保障,该文提出一种结合充电电压片段和等效电路模型(equivalent circuit model,ECM)-数据驱动(data driven method,DDM)融合方法的锂离子电池SOC-SOH-RUL联合估计框架,实现对电池全生命周期的SOC、SOH和RUL的联合估计。首先提取与电池当前容量关联度最高的恒流充电电压曲线片段的上升时间作为健康特征(health factor,HF),利用外部训练集电池的老化数据,离线建立电池老化的最小二乘支持向量机(least squares support vector machine,LSSVM)模型。应用阶段,通过采集待测电池充电电压片段提取HF并代入老化模型进行SOH估计;对该电压区段进行ECM拟合,用阻容参数辨识值和容量估计值建立状态方程和观测方程,结合无迹卡尔曼滤波算法(unscented Kalman filter,UKF)进行SOC估计;用高斯过程回归(Gaussian process regression,GPR)对当前循环次数以前的DV随循环次数的变化进行映射,并借助老化模型预测容量的退化轨迹,实现RUL估计。实验结果表明,所提方法能够联合实现SOC、SOH和RUL的长期稳定估计。展开更多
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.
基金supported in part by the National Natural Science Foundation of China(Nos.52376114,92041001)the Natural Science Foundation of Jiangsu Province(No.BK20200069)the National Science and Technology Major Projects(Nos.J2019-Ⅲ-0015-0059,2017-Ⅲ-0005-0029).
文摘对锂离子电池的荷电状态(state of charge,SOC),健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)进行准确估计是锂离子电池安全稳定运行的重要保障,该文提出一种结合充电电压片段和等效电路模型(equivalent circuit model,ECM)-数据驱动(data driven method,DDM)融合方法的锂离子电池SOC-SOH-RUL联合估计框架,实现对电池全生命周期的SOC、SOH和RUL的联合估计。首先提取与电池当前容量关联度最高的恒流充电电压曲线片段的上升时间作为健康特征(health factor,HF),利用外部训练集电池的老化数据,离线建立电池老化的最小二乘支持向量机(least squares support vector machine,LSSVM)模型。应用阶段,通过采集待测电池充电电压片段提取HF并代入老化模型进行SOH估计;对该电压区段进行ECM拟合,用阻容参数辨识值和容量估计值建立状态方程和观测方程,结合无迹卡尔曼滤波算法(unscented Kalman filter,UKF)进行SOC估计;用高斯过程回归(Gaussian process regression,GPR)对当前循环次数以前的DV随循环次数的变化进行映射,并借助老化模型预测容量的退化轨迹,实现RUL估计。实验结果表明,所提方法能够联合实现SOC、SOH和RUL的长期稳定估计。
