A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train  

作　　者：Canfei SUN Youren WANG Guodong SUN 

机构地区：[1]College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]Testing Center,Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management,Shanghai 201601,China

出　　处：《Chinese Journal of Aeronautics》2020年第5期1549-1561,共13页中国航空学报（英文版）

基　　金：co-supported by the Equipment Pre-research Foundation Project of China (No. JZX7Y20190243016301);Helicopter Transmission Technology Key Laboratory Foundation of China (No. KY-52-2018-0024);the Fundamental Research Funds for the Central Universities & Funding of Jiangsu Innovation Program for Graduate Education under Grant (No. KYLX16_0336)

摘　　要：Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter.During health condition monitoring,the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train.According to actual situation,this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the highdimensional original feature space.In MCFFSA,a fault feature set of multiple domains,including time domain,frequency domain and wavelet domain,is first extracted from the raw vibration dataset.Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets,wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM).Further,Fmeasure is adopted to identify the optimal feature subset,which was employed for subsequent fault diagnosis.The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform.The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA,and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.

关 键 词：Fault detection Feature selection F-MEASURE Helicopter planetary gear train Multi-objective evolutionary algorithm 

分 类 号：V267[航空宇航科学与技术—航空宇航制造工程]