Automated flaw detection scheme for cast austenitic stainless stell weld specimens using Hilbert-Huang transform of ultrasonic phased array data

2012 ◽  
Author(s):  
Tariq Khan ◽  
Shantanu Majumdar ◽  
Lalita Udpa ◽  
Pradeep Ramuhalli ◽  
Susan Crawford ◽  
...  
Keyword(s):  
Phased Array ◽  
Flaw Detection ◽  
Detection Scheme ◽  
Array Data ◽  
Hilbert Huang Transform ◽  
Ultrasonic Phased Array

Study of Feature Extraction Method in Ultrasonic Phased Array Testing for Long-Distance Pipeline

2008 ◽  
Author(s):  
Jian Li ◽  
Xianglin Zhan ◽  
Shili Chen ◽  
Zhoumo Zeng ◽  
Shijiu Jin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Phased Array ◽  
Flaw Detection ◽  
Long Distance ◽  
Feature Extraction Method ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Ultrasonic Phased Array ◽  
Flaw Classification

Though ultrasonic phased array technology is more efficient than traditional manual ultrasonic testing method, automatic flaw classification is a challenge and still hasn’t been well solved. Whether the representative features can be extracted from each type of ultrasonic flaw signal is a key to influencing the accuracy rate of automatic flaw classification. In this paper, second generation wavelet transform (SGWT) is proposed as a flaw feature extraction method, having the advantages of high computation speed, simple structure and occupying less memory. After introducing the principle of SGWT, the SGWT-based feature extraction algorithm is analyzed. Separability measure based on Euclidean distance is introduced as the evaluation criterion to assess flaw feature extraction performance. For comparison, first generation wavelet packet transform (WPT), a common feature extraction method, is also adopted to extract flaw feature. The experiment result is indicated that the classification performance of SGWT-based feature extraction algorithm is improved than WPT-based feature extraction algorithm, and the classification speed of the former is almost two times of the latter, which is valuable for automatic flaw detection and classification of pipeline girth weld.

An Automatic Flaw Identification Method for Defect Inspection of Pipeline Girth Weld

2008 ◽  
Author(s):  
Jian Li ◽  
Xianglin Zhan ◽  
Shili Chen ◽  
Jingchang Zhuge ◽  
Shijiu Jin ◽  
...  
Keyword(s):  
Phased Array ◽  
Flaw Detection ◽  
Inspection System ◽  
Energy Status ◽  
Long Distance ◽  
Feature Extraction Method ◽  
Identification Method ◽  
Ultrasonic Phased Array ◽  
Flaw Classification ◽  
Girth Welds

Various types of defect may be formed in girth welds of long-distance pipeline in the process of welding. They are hidden dangers to pipeline transportation safety. Currently, ultrasonic phased array instrument is commonly adopted for quick automatic positioning and quantitative analysis of flaws in the girth weld after welding. But as for qualitative analysis – flaw classification, traditional manual identification method is still used. By traditional method, human-made error is easily introduced and classification result is depended on the detection experiences of the inspecting person. To overcome these deficiencies, a new method combined second generation wavelet transform (SGWT) with Radial Basis Function neural network (RBFN) is proposed in this paper, realizing automatic flaw classification and reducing human factors impaction. SGWT is ideally matched local characteristics of the flaw signal, improving both the computational speed and flaw classification efficiency. Then, based on the “energy-status” feature extraction method and the above SGWT analysis, feature eigenvectors of the flaw signals are extracted, training the following RBFN. And then when the feature of any flaw is extracted, it can be recognized by the network. The output of the network is the type of the input flaw signal, realizing automatic flaw classification. Finally, an ultrasonic phased array inspection system is described. The system is integrated with automatic flaw detection and classification. Experiments are tested on a long-distance pipeline girth weld block with artificial defects in it. The results validate that the proposed method is efficient, which is helpful to increasing inspection speed and reliability of flaw inspection for long-distance pipeline girth welds.

scholarly journals Automated Flaw Detection in Multi-channel Phased Array Ultrasonic Data Using Machine Learning

2021 ◽  
Vol 40 (3) ◽  
Author(s):  
Oskar Siljama ◽  
Tuomas Koskinen ◽  
Oskari Jessen-Juhler ◽  
Iikka Virkkunen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Speed ◽  
Phased Array ◽  
Flaw Detection ◽  
Data Sets ◽  
Clear Trend ◽  
Array Data ◽  
The Rich ◽  
Rich Data

AbstractModern ultrasonic inspections utilize ever-richer data-sets made possible by phased array equipment. A typical inspection may include tens of channels with different refraction angle, that are acquired at high speed. These rich data sets allow highly reliable and efficient inspection in complex cases, such as dissimilar metal or austenitic stainless steel welds. The rich data sets allow human inspectors to detect cracks with low signal-to-noise ratio from the wider signal patterns. There’s a clear trend in the industry to even richer data sets with full matrix capture (FMC) and related techniques. Convolutional neural networks have recently shown capability to detect flaws with human level accuracy in ultrasonic signals at the B-scan level. To enable automated flaw detection at human-level accuracy for critical applications, these neural networks need be developed to take advantage of today’s rich phased array data-sets. In the present paper, we extend previous work and develop convolutional neural networks that perform highly reliable flaw detection on typical multi-channel phased array data on austenitic welds. The results show, that the modern neural networks can accommodate the rich ultrasonic data and display high flaw detection performance.

scholarly journals Application of the Factorisation Method to Limited Aperture Ultrasonic Phased Array Data

2017 ◽  
Vol 103 (6) ◽  
pp. 954-966 ◽  
Author(s):  
Katherine M. M. Tant ◽  
Anthony J. Mulholland ◽  
Anthony Gachagan
Keyword(s):  
Phased Array ◽  
Array Data ◽  
Ultrasonic Phased Array
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