Abstract：In order to solve the problems of low efficiency and long diagnosis time of traditional vibration signal feature extraction, a rolling bearing fault diagnosis method based on empirical mode decomposition and principal component analysis were proposed. Firstly, the empirical mode decomposition was used to decompose the vibration signal into a finite number of intrinsic mode functions and a residual function to extract the energy of the main intrinsic mode functions and their local average frequency characteristics. Finally, the composite feature vector was used as the input of principal component analysis classifier to complete the fault identification. The experimental results showed that the composite eigenvector can effectively reflect the running state of the bearing. Compared with BP neural network, support vector machine and K-nearest neighbor algorithm, principal component analysis classification not only can accurately identify faults, but also has the advantages of short training time and convenient use.
汪朝海,蔡晋辉,曾九孙. 基于经验模态分解和主成分分析的滚动轴承故障诊断研究[J]. 计量学报, 2019, 40(6): 1077-1082.
WANG Chao-hai,CAI Jin-hui,ZENG Jiu-sun. Research on Fault Diagnosis of Rolling Bearing Based on Empirical Mode Decomposition and Principal Component Analysis. Acta Metrologica Sinica, 2019, 40(6): 1077-1082.
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2019, Vol. 40 
