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

Chinese Journal of Aeronautics 2020年第5期33卷 1549-1561页

作者： Canfei SUN Youren WANG Guodong SUN College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Testing Center Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health ManagementShanghai 201601China

planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the *** health condition monitoring,the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear *** 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 *** MCFFSA,a fault feature set of multiple domains,including time domain,frequency domain and wavelet domain,is first extracted from the raw vibration *** 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 *** effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission *** 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

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