Research on damage detection of large generator stator insulation using guided waves

Chen, Yan; Li, Ruihua; Li, Hao; Hu, Bo

doi:10.1109/ceidp.2013.6747084

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…In this paper, the element is set as a cube with a side length of 1 mm and the time step is set to 0.1 us. The material parameters of the stator insulation were tested as follows [30]: Young’s modulus of 35 GPa, density of 1720 kg/m 3 , and Poisson’s ratio of 0.2. Moreover, PZT wafers are surface-attached on the insulation of stator bar using an adhesive epoxy to establish the active sensor network.…”

Section: Numerical Simulation and Results Analysis Of Stator Insulamentioning

confidence: 99%

“…PZT A1 is served as actuator to excite Lamb waves, and other PZT wafers act as sensors to acquire the Lamb wave signals. According to the material parameters of the stator insulation tested in [30], the dispersion curve of lamb wave propagation can be obtained. According to the previous research [7,9,31], the consensus rules to choose a proper frequency from dispersive cure are that: minimize the propagation modes, large discriminability of group velocity to separate each individual wave mode and stabilized group velocity.…”

Section: Numerical Simulation and Results Analysis Of Stator Insulamentioning

confidence: 99%

“…According to [7,9,30], when the excitation frequency is about 13 kHz, the Lamb wave has the least modes (including only the S0 and A0 wave modes). In addition, the A0 mode wave packet amplitude has a smaller attenuation than the S0 mode wave packet amplitude.…”

Section: Numerical Simulation and Results Analysis Of Stator Insulamentioning

confidence: 99%

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

et al. 2019

Sensors

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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.

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Section: Numerical Simulation and Results Analysis Of Stator Insulamentioning

confidence: 99%

Section: Numerical Simulation and Results Analysis Of Stator Insulamentioning

confidence: 99%

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

et al. 2019

Sensors

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“…The material parameters of stator insulation were tested as: Young’s modulus of 35 Gpa, density of 1720 kg/m 3 and Poisson’s ratio of 0.2. Based on these parameters, the dispersive curves for Lamb wave propagation in stator insulation can be acquired by solving the Rayleigh-Lamb Equations [ 20 , 26 ]. In accordance with [ 20 ], the excitation signal in the experiment adopted a five-cycle Hanning-windowed sinusoidal toneburst with a central frequency of 15 kHz.…”

Section: Stator Insulation Damage Identification Experiments and Rementioning

confidence: 99%

Damage Identification of Large Generator Stator Insulation Based on PZT Sensor Systems and Hybrid Features of Lamb Waves

2018

Sensors

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Large generators are the principal pieces of equipment in power systems, and their operation reliability critically depends on the stator insulation. Damages in stator insulation will gradually lead to the failure and breakdown of generator. Due to the advantages of Lamb waves in Structural health monitoring (SHM), in this study, a distributed piezoelectric (PZT) sensor system and hybrid features of the Lamb waves are introduced to identify stator insulation damage of large generator. A hierarchical probability damage-imaging (PDI) algorithm is proposed to tackle the material inhomogeneity and anisotropy of the stator insulation. The proposed method includes three steps: global detection using correlation coefficients, local detection using Time of flight (ToF) along with the amplitude of damage-scattered Lamb wave, and final images fusion. Wavelet Transform was used to extract the ToF of Lamb wave in terms of the time-frequency domain. Finite Element Modeling (FEM) simulation and experimental work were carried out to identify four typical stator insulation damages for validation, including inner void, inner delamination, puncture, and crack. Results show that the proposed method can precisely identify the location of stator insulation damage, and the reconstruction image can be used to identify the size of stator insulation damage.

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Research of Damage Imaging Algorithm in Plate Based on Signal Magnitude

Luo

Zhu

et al. 2014

Communications in Computer and Information Science

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Research on damage detection of large generator stator insulation using guided waves

Cited by 6 publications

References 4 publications

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

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

Damage Identification of Large Generator Stator Insulation Based on PZT Sensor Systems and Hybrid Features of Lamb Waves

Research of Damage Imaging Algorithm in Plate Based on Signal Magnitude

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