基于参数优化SDP分析的转子故障诊断方法

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摘要针对存在多种故障类型不同故障严重程度的转子故障诊断问题，提出了一种基于参数优化对称点模式（Symmetrized Dot Pattern，SDP）分析的智能诊断方法。首先，利用SDP分析提取多个传感器信号的故障特征并将其融合为SDP图像；然后，以基于欧氏距离定义的图像区分度函数为适应度函数，基于天牛须搜索（Beetle Antennae Search，BAS）算法获得SDP分析中角域增益因子与时间延滞系数的最佳取值；最后，利用SDP图像训练卷积神经网络（Convolutional Neural Network，CNN）获得转子故障诊断模型。实验研究表明，本文方法相较于其他故障诊断方法具有更高的诊断精度，且在强噪声环境下的诊断表现良好。基于BAS算法优化后的SDP分析放大了不同类型不同严重程度转子故障的表征差异，提高了故障诊断精度。
关键词：转子，对称点模式，天牛须搜索算法，卷积神经网络，故障诊断

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	万周1
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	何俊增1
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	姜东2
	3
	李坚4
	张大海1
	2

关键词 ：转子, 对称点模式, 天牛须搜索算法, 卷积神经网络, 故障诊断

Abstract：Based on parameter optimized symmetrized dot pattern(SDP) analysis, an intelligent method is proposed to diagnose rotor faults with various types and severity. Firstly, the fault features of multiple sensor signals were extracted by SDP analysis and fused into SDP images; Then, the image discrimination function defined based on Euclidean distance was used as the fitness function, the optimal values of angle domain gain factor and time lag coefficient in SDP analysis were obtained based on the beetle antennae search algorithm; Finally, the SDP images were used to train the convolutional neural network to obtain the rotor fault diagnosis model. Experimental results show that the diagnosis accuracy of the proposed method was higher than other fault diagnosis methods, and performed well in strong noise environment. The parameter optimized SDP analysis enlarges the characterization differences of rotor faults with various types and severity, and improves the fault diagnosis accuracy.

收稿日期: 2021-11-17 出版日期: 2023-01-15

引用本文:

万周1,2，何俊增1,2，姜东2,3，李坚4，张大海1,2. 基于参数优化SDP分析的转子故障诊断方法[J]. 振动与冲击, 2023, 42(1): 81-88.
WAN Zhou1,2, HE Junzeng1,2, JIANG Dong2,3, LI Jian4, ZHANG Dahai1,2. Rotor fault diagnosis method based on parametric optimization SDP analysis. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(1): 81-88.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I1/81

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