Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion

Hongsheng Yan,Hongfu Zuo,Jianzhong Sun,Di Zhou,Han Wang,Joseph Majdalani

doi:10.1155/2021/5876299

Abstract

Aeroengine is one of the most concerned objects of the relevant aviation industry and researchers, and it is a hard work to assess and predict performance degradation due to the complex structure and the changeable operating condition of the engine. In order to realize the performance degradation assessment and remaining useful life (RUL) prediction of aeroengine, this paper proposes a two-stage assessment and prediction method based on data fusion. First, the standard deviation merged by multiple selected features is used as the health indicator to characterize the engine performance. Second, a sliding window detection method called average local window slope is proposed to determine the current health state of observations by a specified rule. Finally, the RUL prediction is performed on the observation in the two stages, respectively. On the one hand, a similarity-based RUL prediction method is used to engines in the health stage, and on the other hand, for engines in the degradation stage, a RUL prediction method based on a mapping function of the standard deviation and the current using cycle is established. The proposed method has been applied and verified on the NASA’s C-MAPSS simulation data. Results of degradation assessment and prediction show that the proposed method is trustworthy and feasible from the engineering perspective, and it has better performance in the comprehensive indicator compared with other methods.

Highlights

The aeroengine is the core component of modern military and civil aircraft, which performance will inevitably degrade with use
For remaining useful life (RUL) prediction of aircraft engine, considering that the prediction result is greater than the actual available time will lead to maintenance decision fall behind the actual failure occurrence time, which will bring unacceptable unsafe consequences and economic loss
The prediction result is less than the actual value, and the result leads to advanced maintenance works and loss of useful life, which is safer and conservative

Summary

Introduction

The aeroengine is the core component of modern military and civil aircraft, which performance will inevitably degrade with use. Studying a large number of related literature, the research of engine performance degradation assessment and prediction can be divided into three methods: model-based methods, data-driven methods, and deep learning (DL) methods. With the development of monitoring technology and data analysis method, research on engine performance degradation and RUL prediction is depends on restricted traditional degradation distributions and using more and more abundant available monitoring data to realize data-driven model development This type of data-driven engine physical degradation model takes into account both the description of the performance degradation process and the application of monitoring parameters.

Theoretical Background

Proposed Methodology and Algorithms

Degradation Assessment

Implementation and Analysis

Methods

Conclusions