Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Sensors

卷:20

Song Feng¹  Shangqi Zhou¹  Yi Zhang¹  Kai Zheng¹  Bin Zhang¹  Qingqing Huang² 

[1] School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;

[2] School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;

关键词: prognostics;    aircraft engine;    sensor selection;    degradation modeling;    functional principal component analysis;    bayesian inference;   

DOI  :  10.3390/s20030920

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown