Engine performance trending: practical insights

Abstract

A vehicle health usage monitoring system (HUMS) makes it possible to create an accurate picture of the current and expected condition of a propulsion engine. Gas turbine engines, the focus of this paper, convert fuel energy into useful mechanical work by following a well-defined thermodynamic cycle. In this paper, the authors present the principles of engine performance trending, describing them with examples. In engine performance trending, the underlying thermodynamic cycle is analysed using three key steps: cycle characterization using noisy sensor data, interpreting deviations with respect to engine faults and trending the deviations to assess future behaviour. In practical terms, engine performance trending exposes the correct analytic choice based on data and customer requirements. The first half of this paper describes a hierarchy of techniques for characterizing the underlying thermodynamic cycle, from simple energy balance to more complex aerodynamic analysis. Here, we discuss pattern recognition algorithms (eg, fuzzy logic and principal component analysis) for interpreting deviations and the principles of predictive trending. In the second half of the paper, we give examples to describe the practical customization needed to meet the requirements for HUMS. In our final remarks, we discuss open research issues, primarily in the area of constrained parameter estimation, as well as practical validation of engine performance trending. This paper is intended for HUMS practitioners in academia and industry.