少量样本下基于PCA-BNs的多故障诊断

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (4) : 288-296.

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王进花1,2,3，马雪花1，岳亮辉1，安永胜4，曹洁1

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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

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摘要

针对一些工业设备因有标签故障样本数据少而导致诊断准确率低的问题，提出了一种PCA-BNs主成分分析和斯网络结合的多故障网络模型的建模方法。通过PCA对时序信号进行降维，得到相互独立的故障特征，提高提取故障关键信息的能力；利用融合单故障贝叶斯网络构建多故障贝叶斯网络结构的方法，解决BN建模过程耗时的问题；通过高斯分布与极大似然估计结合的方法确定网络参数，提高少量数据BN建模的精度，实现在少量样本下的故障诊断。实验结果表明，基于PCA-BNs的故障诊断方法在少量样本条件下，能实现高精度的故障诊断，并且有效缩减了算法运行时间。

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.

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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[J]. Journal of Vibration and Shock, 2024, 43(4): 288-296

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