| 陈晓梅,王行健,蔡烨,周博.基于AVMD和排列熵的t分布邻域嵌入流形HHO-SVM模拟电路故障诊断方法[J].电子测量与仪器学报,2024,38(6):233-240 |
| 基于AVMD和排列熵的t分布邻域嵌入流形HHO-SVM模拟电路故障诊断方法 |
| Analog fault diagnosis method based on AVMD and t-SNE using HHO-SVM |
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| DOI： |
| 中文关键词: 自适应变分模态分解AVMD t分布邻域嵌入 故障诊断 哈里斯鹰优化支持向量机 |
| 英文关键词:adaptive variational modal decomposition AVMD t-distributed stochastic neighbor embeddings fault diagnosis Harris Hawks optimized support vector machine |
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| 摘要点击次数: 28 |
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| 中文摘要: |
| 随着信息大数据时代的到来，对于电子系统的依赖程度越来越高，因此模拟电路的故障诊断的准确度要求与日俱增。而模拟电路故障诊断困难，是电子系统诊断维修的瓶颈。本文提出基于自适应变分模态分解（AVMD）和排列熵(PE)的t分布邻域嵌入流形哈里斯鹰优化支持向量机（HHO-SVM）模拟电路故障诊断方法。首先，利用AVMD对待测电路的观测信号进行自适应变分模态分解，得到多组IMF信号，不仅可以克服噪声干扰，而且可以来自适应地确定分解模式的数量，进一步提升分解精度；再对IMF计算排列熵，以充分体现IMF不同时段局部特征，二者相结合构建故障特征向量。并在此基础上，采用t分布式随机邻域嵌入（t-SNE）实现特征空间的流形学习和降维，构建具有良好区分度且保留原来的局部结构特征的故障特征向量；最后依靠哈里斯鹰优化支持向量机（HHO-SVM），使其具有良好的分类准确度，从而最终完成电路故障诊断。通过仿真验证，结果显示，本文方法故障诊断正确率可达100%，效果良好。 |
| 英文摘要: |
| In the era of information big data, the dependence degree on analog circuits is getting more severe, which results in the requirement for diagnosis accuracy of analog circuits grow with every passing day. However, analog circuits are very difficult to diagnosis, as a result, it is the bottleneck of electronic system diagnosis and maintenance. In this paper, an IHHO-SVM combining AVMD and PE and manifold learning is put forward. Firstly, adaptive variational modal decomposition AVMD is used to obtain IMF signals from observable signals of circuit under test, which could not only suppress noises disturbance, but also adaptively determine the number of IMF signals and improve the decomposition accuracy. Then IMF signals are computed with permutation entropy (PE) to construct fault features in order to fully reflect the local characteristic of IMF signal at different time span. Based on all these works, t-distributed stochastic neighbor embeddings(t-SNE) is combined to realize dimensionality reduction while remaining excellent discrimination power of fault features vectors, with the new feature vector formed at last. Finally, Harris Hawks algorithm is combined to optimize the support vector machine, which is called HHO-SVM here, for fault classification. The simulation tests show that the algorithm proposed in this paper has an excellent accuracy of 100%. |
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