PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Abstract

As the heart of an aircraft, the aircraft engine's condition directly affects the safety, reliability, and operation of the aircraft. Prognostics and health management for aircraft engines can provide advance warning of failure and estimate the remaining useful life. However, aircraft engine systems are complex with both intangible and uncertain factors, it is difficult to model the complex degradation process, and no single prognostic approach can effectively solve this critical and complicated problem. Thus, fusion prognostics is conducted to obtain more accurate prognostics results. In this paper, a prognostics and health management-oriented integrated fusion prognostic framework is developed to improve the system state forecasting accuracy. This framework strategically fuses the monitoring sensor data and integrates the strengths of the data-driven prognostics approach and the experience-based approach while reducing their respective limitations. As an application example, this developed fusion prognostics framework is employed to predict the remaining useful life of an aircraft gas turbine engine based on sensor data. The results demonstrate that the proposed fusion prognostics framework is an effective prognostics tool, which can provide a more accurate and robust remaining useful life estimation than any single prognostics method.Index Terms-Fusion prognostics, sensor data, prognostics and health management, remaining useful life, aircraft engines.

Multidimensional prognostics for rotating machinery: A review

“…Xu et al 112 successfully employed RNNs, SVMs and DSR to estimate the RUL of an aircraft gas turbine. An echo state network (ESN), which is a variant of RNNs, was employed by Peng et al 113 to predict the RULs of engines using National Aeronautics and Space Administration (NASA) repository data.…”

Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model

“…Subject to the recent advancements in renewable energy, GT are now expected to operate more efficiently under part-load operating conditions. Either under part-load or peak load conditions, GTs are also required to operate flexibly with relatively higher number of firing start-ups and shut downs, which reduces the time between failures [1,2]. Therefore, the maintenance strategy for such system should be robust and flexible in order to have improved reliability and better availability that would result in less downtime and operating cost [3].…”

“…In recent years a new concept of maintenance called "prognostics based maintenance" that would potentially minimize risk while maximizing useful life has been reported [1]. This approach is based on the condition monitoring data and involves prediction of likely fault cases instead of waiting until sudden failure occurs.…”

“…These include support vector machines (SVM) [1,[7][8][9], Bayesian forecasting [10][11][12], Kalman Filters [10], state-space models [12], artificial neural networks [13][14][15][16], independent component analysis [17], regression techniques [18], Dempster-Shafer regression [1] and one parameter double exponential smoothing [10]. These techniques can be classified into two types: (i) physics based modelling approaches, and (ii) data driven models [5,[19][20][21].…”

“…These techniques can be classified into two types: (i) physics based modelling approaches, and (ii) data driven models [5,[19][20][21]. Physics based models are effective in dealing with novel faults but are considered difficult to implement to complex systems [1] since they require detail knowledge about the system. In the context of dynamic condition, it may require real-time parameter optimization, which is a limiting factor since GT is featured by many interacting parameters [21].…”