State-of-the-Art Portable Measurement and Defect Detection Technology for Coiled-Tubing String

2013 ◽  
Author(s):  
Aaron Stayer ◽  
Mirjam Zwanenburg ◽  
Nicolas Scuadroni ◽  
Andrew S Zheng ◽  
Zhanke Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
State Of The Art ◽  
Coiled Tubing ◽  
Detection Technology ◽  
Tubing String

Study on Defect Detection Technology for Bridge Deck Pavements

2017 ◽  
Vol 19 (6) ◽  
pp. 38
Author(s):  
Chengchao Guo ◽  
Pengfei Xu ◽  
Can Cui
Keyword(s):  
Defect Detection ◽  
Bridge Deck ◽  
Detection Technology

State-of-the-Art Coiled Tubing Technology at Prudhoe Bay, Alaska

2007 ◽  
Author(s):  
Jennifer Yvonne Julian ◽  
Kirk Charles Forcade ◽  
Taylor L. West ◽  
Kevin yeager ◽  
Robert Lee Mielke ◽  
...  
Keyword(s):  
State Of The Art ◽  
Coiled Tubing ◽  
Prudhoe Bay

New Defect Detection Technology for Tissue Industry

2014 ◽  
Vol 68 (3) ◽  
pp. 311-313
Author(s):  
Jason Zyglis ◽  
Wayne Killmer ◽  
Atsushi Kurosaki
Keyword(s):  
Defect Detection ◽  
Detection Technology

scholarly journals Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1459 ◽  
Author(s):  
Tamás Czimmermann ◽  
Gastone Ciuti ◽  
Mario Milazzo ◽  
Marcello Chiurazzi ◽  
Stefano Roccella ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Processing ◽  
State Of The Art ◽  
Industrial Applications ◽  
The State ◽  
Shape Error ◽  
Supervised Classifiers ◽  
Processing Techniques

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

Recognizing Coiled Tubing Defect Characteristics Based on Weak Magnetic Detection Technology

2019 ◽  
Vol 55 (9) ◽  
pp. 1-6 ◽  
Author(s):  
Zhaoming Zhou ◽  
Mian Qin ◽  
Fu Wan ◽  
Zhongxuan Han ◽  
Jiajia Jing
Keyword(s):  
Coiled Tubing ◽  
Detection Technology ◽  
Magnetic Detection

Feature weighting network for aircraft engine defect detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2050012
Author(s):  
Liqiong Chen ◽  
Lian Zou ◽  
Cien Fan ◽  
Yifeng Liu
Keyword(s):  
Defect Detection ◽  
State Of The Art ◽  
Aircraft Engine ◽  
Air Transportation ◽  
Feature Weighting ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Feature Pyramid ◽  
New Feature

Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications.

scholarly journals Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing

2019 ◽  
Vol 10 (1) ◽  
pp. 235 ◽  
Author(s):  
Hongyao Shen ◽  
Wangzhe Du ◽  
Weijun Sun ◽  
Yuetong Xu ◽  
Jianzhong Fu
Keyword(s):  
Feature Extraction ◽  
3D Printing ◽  
Defect Detection ◽  
Point Cloud ◽  
Fused Deposition Modeling ◽  
Surface Defects ◽  
Detection Accuracy ◽  
Feature Extraction Method ◽  
Fused Deposition ◽  
Detection Technology

Fused Deposition Modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts in the printing process. In the existing defect detection technology, the characteristics of parts themselves may be misjudged as defects. This paper presents a solution to the problem of distinguishing the defects and their own characteristics in robot 3-D printing. A self-feature extraction method of shape defect detection of 3D printing products is introduced. Discrete point cloud after model slicing is used both for path planning in 3D printing and self-feature extraction at the same time. In 3-D printing, it can generate G-code and control the shooting direction of the camera. Once the current coordinates have been received, the self-feature extraction begins, whose key steps are keeping a visual point cloud of the printed part and projecting the feature points to the picture under the equal mapping condition. After image processing technology, the contours of pictured projected and picture captured will be detected. At last, the final defects can be identified after evaluation of contour similarity based on empirical formula. This work will help to detect the defects online, improve the detection accuracy, and reduce the false detection rate without being affected by its own characteristics.

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