Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements

作     者：Sarkar, Soumik Jin, Xin Ray, Asok 

作者机构：Penn State Univ Dept Mech Engn University Pk PA 16802 USA 

出 版 物：《JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME》 (美国机械工程师学会汇刊: 燃气轮机与动力工程杂志)

年 卷 期：2011年第133卷第8期

页      面：81602-81602页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 

基　　金：NASA [NNX07AK49A] U.S. Army Research Office [W911NF-07-1-0376] 

主　　题：aircraft gas turbine engines data-driven fault detection optimal feature extraction multiclass classification 

摘      要：An inherent difficulty in sensor-data-driven fault detection is that the detection performance could be drastically reduced under sensor degradation (e. g., drift and noise). Complementary to traditional model-based techniques for fault detection, this paper proposes symbolic dynamic filtering by optimally partitioning the time series data of sensor observation. The objective here is to mask the effects of sensor noise level variation and magnify the system fault signatures. In this regard, the concepts of feature extraction and pattern classification are used for fault detection in aircraft gas turbine engines. The proposed methodology of data-driven fault detection is tested and validated on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) test-bed developed by NASA for noisy (i.e., increased variance) sensor signals. [DOI: 10.1115/1.4002877]