Research On Remaining Useful Life Prognostics For Aero-engine Based On ACARS

In order to meet the development of native aircraft industry, guarantee the safety of air transportation and control the maintenance cost of aircraft, Remaining Useful Life(RUL) prognostics is required as one of the core part of Prognostics and Health Management(PHM) for aero-engine. Based on the monitoring parameters obtained from Aircraft Communication Addressing and Reporting System(ACARS) and the analysis of performance degradation, the degradation model is established and the corresponding algorithms are designed, which contributes to the RUL prognostics.Firstly, the standards and protocols for the transmission of the monitoring dataobtained from ACARSare analyzed, the preprocessing and the transcodingof the ACARS data are performed, and the key monitoring parameters which are utilized in the following prognostics are determined.Secondly, the RUL prognostics algorithm for aero-engine is designed based on Kalman filtering method. In the algorithm, the key monitoring parameters are fused in stages, the performance degradation model is established based on state-space method, and Kalman filtering is applied in the parameters estimation of the model. A comparison is made on the prognostics by multi-stage linear fusion based Kalman filtering and the prognostics by single stage linear fusion based Kalman filtering. It is shown in the example analysis that the estimated RUL distribution and the prognostics evolutionary process of the multi-stage linear fusion based Kalman filtering is superior.Thirdly, in consideration of the nonlinear mapping relationship between multiple monitoring parameters and the health state of the aero-engine, the RUL prognostics algorithm is designed based on Particle filtering method. The fusion of the monitoring parameters is performed by multi-stage and nonlinear method, the performance degradation model is established based on state-space method, and Particle filtering is applied in the parameters estimation of the model. A comparison is made on the prognostics by multi-stage nonlinear fusion based Particle filtering, the prognostics by multi-stage linear fusion based Particle filtering, and the prognostics by single stage linear fusion based Particle filtering. The example shows that the estimated RUL distribution and the prognostics evolutionary process of the multi-stage nonlinear fusion based Particle filtering is optimal.Lastly, the aero-engine remaining useful life prognostics system is designed based on MATLAB, which accomplishes the following functions, the fusion of monitoring data, the construction of the performance degradation model, and the implementation of the prognostics.