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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).

Machine learning (ML) is a data-driven approach to discovering patterns and knowledge, and it is different from the physics-based approach, which uses the principles of physics to describe a phenomenon. Physics-based approach has dominated the field of engine diagnostics because of the maturity of scientific and engineering knowledge embodied in the design and manufacturing of the engine and its components. Nevertheless, development of ML techniques has accelerated in the last three decades, and the techniques can potentially lower development time and are applicable to a wide variety of industries. This paper examines some of the most commonly cited ML techniques for handling numerical data and applies them to a gas turbine engine diagnostic problem. The diagnostic problem is to isolate the symptom of engine performance degradations to a root-cause fault or failure. This fault isolation problem is a type of classification problem in the ML world. A hypothetical engine model for commercial airplanes is used in simulation to create a standard dataset. This dataset is then used by all of the selected techniques. The results from the ML algorithms are evaluated in terms of classification accuracy and misclassification rates.

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This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a modelbased approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.

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