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基于兰姆波检测和支持向量机方法的大型发电机定子绝缘多特征融合与损伤识别

Multi-feature Fusion and Damage Identification of Large Generator Stator Insulation Based on Lamb Wave Detection and SVM Method.

作者信息

Li Ruihua, Gu Haojie, Hu Bo, She Zhifeng

机构信息

Department of Electrical Engineering, Tongji University, Shanghai 201804, China.

出版信息

Sensors (Basel). 2019 Aug 29;19(17):3733. doi: 10.3390/s19173733.

DOI:10.3390/s19173733
PMID:31470530
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6749468/
Abstract

Due to the merits of Lamb wave to Structural Health Monitoring (SHM) of composite, the Lamb wave-based damage detection and identification technology show a potential solution for the insulation condition evaluation of large generator stator. This was performed in order to overcome the problem that it is difficult to effectively identify the stator insulation damage the using single feature of Lamb wave. In this paper, a damage identification method of stator insulation based on Lamb wave multi-feature fusion is presented. Firstly, the different damage features were extracted from time domain, frequency domain, and fractal dimension of lamb wave signals, respectively. The features of Lamb wave signals were extracted by Hilbert transform (HT), power spectral density (PSD), fast Fourier transform (FFT), and wavelet fractal dimension (WFD). Then, a machine learning method based on support vector machine (SVM) was used to fuse and reconstruct the multi-features of Lamb wave and furtherly identify damage type of stator insulation. Finally, the effect of typical stator insulation damage identification is verified by simulation and experiment.

摘要

由于兰姆波在复合材料结构健康监测（SHM）方面的优点，基于兰姆波的损伤检测与识别技术为大型发电机定子绝缘状态评估提供了一种潜在的解决方案。这样做是为了克服仅使用兰姆波单一特征难以有效识别定子绝缘损伤的问题。本文提出了一种基于兰姆波多特征融合的定子绝缘损伤识别方法。首先，分别从兰姆波信号的时域、频域和分形维中提取不同的损伤特征。通过希尔伯特变换（HT）、功率谱密度（PSD）、快速傅里叶变换（FFT）和小波分形维（WFD）提取兰姆波信号的特征。然后，采用基于支持向量机（SVM）的机器学习方法对兰姆波的多特征进行融合与重构，进而识别定子绝缘的损伤类型。最后，通过仿真和实验验证了典型定子绝缘损伤识别的效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/a8455b39c18e/sensors-19-03733-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/b406ab77ffef/sensors-19-03733-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/3517327c46f8/sensors-19-03733-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/6d22a5161ba4/sensors-19-03733-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/5598ee7b2f14/sensors-19-03733-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/dbdb1fa4bc87/sensors-19-03733-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/b44ac359ed0c/sensors-19-03733-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/8b13c3b584fa/sensors-19-03733-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/097c017055b2/sensors-19-03733-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/c0973c47cbdd/sensors-19-03733-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/114258553272/sensors-19-03733-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/a8455b39c18e/sensors-19-03733-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/b406ab77ffef/sensors-19-03733-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/3517327c46f8/sensors-19-03733-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/6d22a5161ba4/sensors-19-03733-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/5598ee7b2f14/sensors-19-03733-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/dbdb1fa4bc87/sensors-19-03733-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/b44ac359ed0c/sensors-19-03733-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/8b13c3b584fa/sensors-19-03733-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/097c017055b2/sensors-19-03733-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/c0973c47cbdd/sensors-19-03733-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/114258553272/sensors-19-03733-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccfc/6749468/a8455b39c18e/sensors-19-03733-g011.jpg

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