Denoising of Phased Array Ultrasonic Total Focus Image on Rail Bottom Welds
2021 IEEE Far East NDT New Technology & Application Forum (FENDT)（2021）
Institute of physical science and technology
Abstract
The rail bottom weld area is prone to cracks, which can cause rail break. When using the ultrasonic total focus method(TFM) to detect rail bottom welds, there are a lot of background noise in the image due to the material impurities in the weld area, resulting in unobvious defect characterization. In this paper, the band-pass filtering method is used to filter and denoise the data collected in the full matrix capture(FMC) mode. Then, according to the phase distribution characteristics of the ultrasonic detection signal, the phase coherence factor is used to weight the image on the basis of the filtering to further improve the image signal-to-noise ratio. A 64-element probe is used to image the B-type phased array test block. The results show that this algorithm can effectively improve the image signal-to-noise ratio without weaking the defect signals. Under the same test conditions, the vertical groove defects on the bottom surface of the rail weld were detected and imaged. The results show that this method has a good suppression effect on the background noise in welds, the signal-to-noise ratio has been greatly improved, and can eliminate the artifacts of crack defects.
MoreTranslated text
Key words
Rail bottom weld,TFM,Band-pass filtering,Phase coherence weighting
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined