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.
Highlights
IntroductionIn long-term service, stator insulation is exposed to a combination of electrical, thermal, and mechanical stresses
Stator insulation of large generators has a laminated composite structure
A damage identification method of stator insulation based on Lamb wave multi-feature fusion is proposed
Summary
In long-term service, stator insulation is exposed to a combination of electrical, thermal, and mechanical stresses. They gradually cause the aging of insulation structure, resulting in internal or surface insulation damages, such as void, delamination, puncture crack, and surface crack, etc. These damages eventually can lead to failure and breakdown of large generator stator insulation [1,2,3,4]. Timely and accurate detection and identification of stator insulation structure damage can provide effective and reliable reference information for insulation status diagnosis and life assessment of large generator stator
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call