ORCID Profile
0000-0002-6662-4161
Current Organisation
Xi'an Jiaotong University
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Publisher: Springer Science and Business Media LLC
Date: 11-11-2019
Publisher: Elsevier BV
Date: 04-2021
Publisher: AIP Publishing
Date: 04-2020
DOI: 10.1063/1.5136269
Abstract: At present, denoising parameters in different signal processing algorithms require a specific signal waveform to be set. Human factors would significantly affect the denoising result. To solve this problem, we proposed a signal adaptive denoising method based on a denoising autoencoder to achieve denoising on ultrasonic signals. By applying this method to s le signals and comparing with the singular value decomposition (SVD), principal component analysis (PCA), and wavelet algorithms, it is found that this method can effectively suppress the noise at different noise intensities. Using the signal to noise ratio, root mean square error, and autocorrelation coefficient as evaluation parameters in the experiment, the overall denoising effect of the proposed method is better than that of PCA, and this method is better than the wavelet and SVD algorithms having a relatively weak noise intensity. In addition, by comparing the reconstructed signal curve of the proposed method and that of the wavelet algorithm, the proposed method can retain the information of signal saltation with a better performance. Finally, we apply this method for processing ultrasonic signals and verify its effectiveness from time and frequency domain diagrams.
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 12-2020
Publisher: AIP Publishing
Date: 11-2022
DOI: 10.1063/5.0103844
Abstract: During the radiographic testing with multiple voltage exposures, the problem of image redundancy has serious influences on the speed of analysis. In this paper, by the calculation of spatial entropy and the extraction of microtopography features, a new segmentation method for an unpredictable free-form surface in turbine blades is presented to reduce the testing redundancy. First, the entropy calculation is applied to a set of radiography images of the target object. The optimized image for surface segmentation is determined according to global entropy values and exposure parameters. Then, the obtained image is set as the reference image for the segmentation process. Through the spatial entropy calculation, the reference radiographic image is ided into several regions by geometric features of the tested object. The different thickness range of free-form surfaces is indicated by those regions through the analysis of spatial entropy distribution in the reference image. By the statistical advantage of entropy calculation, the selected region is self-adaptive to the unpredictable free-form surface in the blade. A nickel-based alloy turbine blade is used to validate the segmentation method in the radiographic testing. The processed image quality is assessed by using the American Society for Testing and Materials image quality indicator to address its capability for the detection of defects, where the resolution of the image is not affected by the down-s led effect of entropy calculation. The experimental results show that the image redundancy in the multiple exposure testing is reduced to less than 30% during the inline testing, while the dynamic range in each extracted region with an optimal image is significantly improved.
Publisher: Elsevier BV
Date: 02-2020
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Zhongyu Shang.