AI Article Synopsis
- Lamb wave technology shows promise for evaluating the insulation condition of large generator stators, addressing the challenge of accurately identifying stator insulation damage with a single feature.
- A new damage detection method is introduced that utilizes multi-feature fusion from Lamb wave signals, extracting different characteristics using techniques like Hilbert transform and fast Fourier transform.
- A machine learning approach, specifically support vector machine (SVM), is employed to combine these features and identify the type of insulation damage, which is validated through simulations and experiments.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749468 | PMC |
| http://dx.doi.org/10.3390/s19173733 | DOI Listing |
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