Abstract
Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.
Highlights
As the heart of an aircraft, the engine consists of several subsystems with millions of parts, and its health condition directly impacts operation and safety of the whole aircraft system
The sensor data of the aircraft gas turbine engine degradation is generated from the commercial modular aero-propulsion system simulation (C-MAPSS) developed at NASA [37] and published online for research investigations
To model and track the complex degradation pattern of aircraft engines for accurate and efficient prognosis, a novel method for informative sensor selection and adaptive degradation track is proposed in this paper
Summary
As the heart of an aircraft, the engine consists of several subsystems with millions of parts, and its health condition directly impacts operation and safety of the whole aircraft system. A number of sensors of varying types have been widely used to monitor the health degradation of aircraft engines, which creates a multi-sensor environment for operational health analysis and maintenance decision-making. Prognostics that utilizes monitoring sensors information to predict the future health and estimates the remaining useful life (RUL) before failure/end-of-life has attracted increasing attention from academic researchers and industrial operators [2]. Sensors 2020, 20, 920 reducing unnecessary maintenance and minimizing operational costs, prognostics have been an active research field for aircraft engine applications [3]. For model-based methods, a mathematical model that can describe the engine health degradation process that is normally required to be constructed according to physical failure characteristics before prediction. Some typical model-based methods have been developed for engine health estimation and RUL prediction, such as the Markov model-based [4,5]
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