Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators

Abstract

The aircraft engines manufacturing industry is subjected to many dependability constraints from certification authorities and economic background. In particular, the costs induced by unscheduled maintenance and delays and cancellations impose to ensure a minimum level of availability. For this purpose, Prognostics and Health Management (PHM) is used as a means to perform online periodic assessment of the engines' health status. The whole PHM methodology is based on the processing of some variables reflecting the system's health status named Health Indicators. The collecting of HI is an on-board embedded task which has to be specified before the entry into service for matters of retrofit costs. However, the current development methodology of PHM systems is considered as a marginal task in the industry and it is observed that most of the time, the set of HI is defined too late and only in a qualitative way. In this paper, the authors propose a novel development methodology for PHM systems centered on an anticipated model-based validation of HI. This validation is based on the use of uncertainties propagation to simulate the distributions of HI including the randomness of parameters. The paper defines also some performance metrics and criteria for the validation of the HI set. Eventually, the methodology is applied to the development of a PHM solution for an aircraft engine actuation loop. It reveals a lack of performance of the original set of HI and allows defining new ones in order to meet the specifications before the entry into service.

Prediction of Remaining Useful Life for Aircraft Engines Using Convolutional and Recurrent Neural Networks

“…In this way, the maintenance program can be implemented effectively while scheduling the availability of aircraft on time to reduce costs. The use of forecasting, the ability to predict faults and failures, have made a comeback since the 1990's [25].…”

“…The purpose of PHM is to improve the availability of the system for the operator by performing diagnostics and prognostics from a given set of variables that reflect the health status of the system [25]. The selected health indicators (HI) must be able to:…”

Remaining Useful Life Prediction of a Turbofan Engine Using Deep Layer Recurrent Neural Networks

“…The model-basedtechnique is easy and fast to implement, and the model can be reused. However, model development requires a thorough understanding of the system and high-fidelity models can be computationally intensive[28] [29].…”