Title :
Research on aircraft engine fault detection based on support vector machines
Author :
Heng, Hongjun ; Zhang, Jing ; Xin, Cong
Author_Institution :
Coll. of Comput. Sci. & Technol., Civil Aviation Univ. of China, Tianjin, China
Abstract :
QAR is always applied to monitor and collect most data of flight quality of a plane. In this paper, several aero-engine related key parameters recorded by QAR are analyzed taking the advantage of SVM classifiers for fault diagnosis and QAR threshold analyzing model. The effectiveness and feasibility of the model constructed in this paper is verified by the data collected in the aero-engine detection experiment, which provides a good solution to the fault detection automatically.
Keywords :
aerospace engines; fault diagnosis; mechanical engineering computing; support vector machines; QAR threshold analyzing model; SVM classifiers; aero-engine detection; aircraft engine fault detection; fault diagnosis; flight data monitoring; flight quality data collection; quick access recorder; support vector machines; Aircraft propulsion; Classification algorithms; Data models; Educational institutions; Kernel; Support vector machines; Training; QAR; Support Vector Machine (SVM); fault diagnosis;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4577-1414-6
DOI :
10.1109/CECNet.2012.6202010
