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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.

Lamb waves are used to locate any damage in the stator insulation structure of large generators. However, it is difficult to extract the features of Lamb wave signals in a strong background noise environment, thus significantly reducing the accuracy with which the damage is located. This paper proposes a method based on variational mode decomposition (VMD) and wavelet transform to enhance and extract the location features of stator insulation damage signals of large motors. First, considering that the characteristics of VMD are sensitive to noise, the Lamb wave detection signal is decomposed, denoised, and reconstructed; the reconstructed signal is then wavelet-transformed to extract the time of flight (TOF) of the damage-scattered wave as the damage location feature; finally, the damage location is determined using the TOF features. The proposed method is experimentally tested and verified under various noise environments. The results show that the VMD and wavelet transform methods can significantly improve the signal-to-noise ratio of Lamb wave detection signals and the accuracy with which the damage is located under strong background noise. This study extends the applicability of Lamb wave-based non-destructive detection of stator insulation damage in complex environments.

The large generator faults are mostly from the stator insulation, which is aged by exposure to a combination of thermal, electrical, mechanical, and harsh environment stresses. Condition evaluation of stator insulation is an important measure of ensuring the safe operation and extending the remaining life of large generator. In this paper, a diagnosis method based on the partial discharge (PD) measurement was used for the condition evaluation of the stator insulation. The statistical parameters of partial discharge phase resolved distribution were proposed to assess the aging condition of stator insulation. A partial least square (PLS) approach was used to explore and extract relationships between the statistical parameters of partial discharge distribution with physical properties and performance such as electrical breakdown strength. Results of the PD testing and statistical analysis show that the statistical parameter Sk extent of partial discharge distribution changes much more remarkably with the aging time than do the other parameters. The statistical parameter Sk can be considered as a potential indicator of stator insulation aging. The PLS approach can effectively assess the stator insulation aging condition and can provide a predictive capability for the stator insulation diagnosis.