A Feature Extraction Method for Fault Classification of Rolling Bearing based on PCA

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Wang, Fengtao^1 ; Sun, Jian^1 ; Yan, Dawen^2 ; Zhang, Shenghua^1 ; Cui, Liming^1 ; Xu, Yong^3

Institute of Vibration Engineering, Dalian University of Technology, Dalian, China^1

School of Mathematical Sciences, Dalian University of Technology, Dalian, China^2

West Pacific Petrochemical Company Ltd, Dalian, China^3

关键词: Fault feature selections;    Feature extraction methods;    Fourier transformations;    Fuzzy c-means models;    Hilbert transformations;    Real time performance;    Statistical features;    Time frequency domain;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012079/pdf
DOI  :  10.1088/1742-6596/628/1/012079

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

This paper discusses the fault feature selection using principal component analysis (PCA) for bearing faults classification. Multiple features selected from the time-frequency domain parameters of vibration signals are analyzed. First, calculate the time domain statistical features, such as root mean square and kurtosis; meanwhile, by Fourier transformation and Hilbert transformation, the frequency statistical features are extracted from the frequency spectrum. Then the PCA is used to reduce the dimension of feature vectors drawn from raw vibration signals, which can improve real time performance and accuracy of the fault diagnosis. Finally, a fuzzy C-means (FCM) model is established to implement the diagnosis of rolling bearing faults. Practical rolling bearing experiment data is used to verify the effectiveness of the proposed method.