基于状态空间模型的飞机APU在翼RUL预测方法

为解决利用飞机辅助动力装置(APU)在翼监测数据难以表征其性能状态而造成的性能评估以及剩余使用寿命预测(RUL)难的问题,本文提出一种基于状态空间模型(SSM)与卡尔曼滤波融合的APU在翼RUL预测方法.首先,通过在翼监测数据构造含噪声的性能指标(PI)来表征APU的性能状态,借助维纳过程与建立的含噪声的PI构建状态方...

On-wing RUL prediction method of aircraft APU based on state space model

The on-wing monitoring data of aircraft auxiliary power unit (APU) are difficult to characterize its performance states, whichwill lead to the difficulty that the performance evaluation and remaining useful life (RUL) prediction of the APU is difficult to carry out.To solve this problem, a performance evaluation and RUL prediction approach of APU is proposed based on the state space model( SSM) and Kalman filter (KF). Firstly, a performance indicator (PI) containing noise is constructed from the on-wing monitoring datato characterize the performance state of the APU. The performance degradation process of APU is described by state equation, which isconstructed with the help of the Wiener process and the constructed PI with noise contained. Then, the KF state estimation andprediction method is applied to the SSM. Through estimating the on-wing performance state of APU, the purpose of predicting RUL isachieved. Finally, the APU on-wing monitoring data from the operation of an airline company in China are adopted to conduct thecomprehensive verification and evaluation of the proposed method. Experiment results show that compared with ELM and OptimizedELM, the prediction absolute percentage error of the proposed method is reduced by 72. 1% and 67. 9% , respectively. In addition,compared with the experiment results of other three kinds of methods, the prediction absolute percentage error of the proposed method isreduced by 69. 2% at least. The proposed method can effectively predict the RUL of an on-wing APU, which can provide a reference forthe operation and maintenance personnel to plan maintenance and repair reasonably. More importantly, the method can improve thecomfort of the passengers and the aircraft safety to a certain degree.