Recent Advancements in Aircraft Engine Health Management (EHM) Technologies and Recommendations for the Next Step

Abstract

This paper summarizes the findings of a survey of recent advancements in aircraft Engine Health Management (EHM) technologies. The survey has been motivated by the desire to understand new technologies and application trends in EHM, especially in the last few years, when EHM related research and development (R&D) has increased significantly. Although the R&D primarily covers four areas: system partition, system architecture, EHM functionalities, and algorithmic approaches, the latter two have represented the majority of the published work; hence they are the focus of this paper. While recent advancements are providing building blocks for continued maturation of EHM technologies in the future, the survey has revealed a fundamental inconsistency in defining and representing EHM problems. This inconsistency creates barriers in exchanging EHM-related ideas and results; it also undermines the effectiveness of multi-organizational cooperation. Hence another purpose of this paper is to recommend a consistent approach to treating the problems that EHM is trying to address. Finally, the survey recognizes the need for a unified framework for comparing the performance of various algorithms for solving various types of EHM problems. The author hence suggests that an industry review, modeled after the industry review for multi-variable engine controls in late 1970s, be held. This EHM Industry Review is aimed at defining a series of theme problems for EHM and invite experts in the industry and academia to apply their expertise to solving these problems. The Industry Review will be concluded with a conference to present the results and sharing experience. Traditionally, engine condition monitoring has led the way for condition-based maintenance and health management technologies because of the safety and dispatch requirements of aircraft engines. Hence, the author believes that the results, conclusions, and recommendations presented in this paper can be generalized to all types of equipment, systems, and vehicles, i.e., to EHM (Equipment Health Management), IVHM (Integrated Vehicle Health Management) and ISHM (Integrated System Health Management).

Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle

“…Aero engine health management (EHM) is a widely researched field due to its operational reliability and maintenance costs [1,2]. Accuracy of most health management systems relies on accuracy of measurements acquired from sensors [3].…”

Nonlinear control study on aircraft engine using a generalized minimum variance approach

“…2 However, today, the number of advanced propulsion system control variables has also been reached 15-25. 3,4 The F119 has eight control variables in 1990s, 5 and the PW1128 engine equipped in the F16-XL also has up to 24 control variables. 6 The linear control theories about multivariable are abundant, such as linear quadratic Gaussian (LQG) or loop transfer recovery (LTR) [7][8][9] and H N robust control.…”

Gas Turbine Health State Determination: Methodology Approach and Field Application

“…The optimization of maintenance management, which should lead to cost saving and increase in machine availability, can be performed by supporting gas turbine preventive maintenance (which comes from manufacturer experience in terms of component life and performance degradation versus working hours and is performed according to a priori schedules, regardless of the effective gas turbine health state) with on-condition maintenance, which consists of "ad hoc" actions descending from gas turbine actual operating state [1][2][3][4][5][6][7]. Therefore, On-condition maintenance requires up-todate knowledge of the machine health state in real time.…”