Multiple fault diagnosis based on PCA-BNs with a small number of samples
WANG Jinhua1，2，3, MA Xuehua1, YUE Lianghui1, AN Yongsheng2, CAO Jie1
1.College of Electrical & Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China;
3.National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
4.Gansu Huacheng Construction and Installation Engineering Group Co.,Ltd., Lanzhou 730070, China
Abstract：Aiming at the low diagnosis accuracy of some industrial equipment due to the lack of labeled fault sample data, a modeling method of multi fault network model based on PCA-BNs principal component analysis and Bayesian network (PCA-BNs) is proposed. The dimensionality of time series signals is reduced by PCA to obtain independent fault features and improve the ability of extracting key fault information; The method of fusing single fault Bayesian network to construct multi fault Bayesian network structure is used to solve the time-consuming problem of BN modeling process; The combination of Gaussian distribution and maximum likelihood estimation is used to determine the network parameters, improve the accuracy of BN modeling with a small amount of data, and realize fault diagnosis under small samples. The experimental results show that the PCA-BNs fault diagnosis method proposed in this paper can achieve high-precision fault diagnosis under the condition of small samples, and effectively reduce the running time of the algorithm.
收稿日期: 2022-11-28
出版日期: 2024-02-28
引用本文:
王进花1,2,3，马雪花1，岳亮辉1，安永胜4，曹洁1. 少量样本下基于PCA-BNs的多故障诊断[J]. 振动与冲击, 2024, 43(4): 288-296.
WANG Jinhua1，2，3, MA Xuehua1, YUE Lianghui1, AN Yongsheng2, CAO Jie1. Multiple fault diagnosis based on PCA-BNs with a small number of samples. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(4): 288-296.
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