New method of identification of false or nuisance defects using defect imaging system DIS-05

2007 ◽  
Author(s):  
Hao Zhang ◽  
Katsuyuki Takahashi ◽  
Hideaki Bando ◽  
Yasunobu Kitayama ◽  
Akio Sugano ◽  
...  
Keyword(s):  
Imaging System ◽  
New Method ◽  
Defect Imaging

A new method of ICCD imaging system MRC measurement

2005 ◽  
Author(s):  
Sheng-cai Li ◽  
Wei-qi Jin ◽  
Xia Wang ◽  
Wei-qiang Zhang ◽  
Hai-yan Dong
Keyword(s):  
Imaging System ◽  
New Method

Ultrasonic Testing and Photographic Imaging System for Tube to Tubesheet Weld of Heat Exchanger

2017 ◽  
Author(s):  
Masamitsu Abe ◽  
Joichi Murakami ◽  
Naoto Shinmura
Keyword(s):  
Heat Exchanger ◽  
Ultrasonic Testing ◽  
Imaging System ◽  
New Method ◽  
Secular Change ◽  
System Software ◽  
Imaging Device ◽  
Computer Screen ◽  
Periodic Inspection ◽  
Visual Testing

This paper describes non-destructive testing systems that were constructed to inspect tube-to-tubesheet welds of a heat exchanger operating in a chemical plant such as urea products. A phased array ultrasonic testing system inserts a testing device into the tube, rotates it and scans the tube-to-tubesheet welds, thereby detecting any weld defects that are not only parallel to a tube axis but also in any direction, and displaying the size and location of the defects on a computer screen using system software. These welds have conventionally been inspected by liquid penetrant testing and visual testing as surface inspections during fabrication at shop and periodic inspection at plant site. This new method, however, provides a practical way to the inspection of weld inside. In addition, in a photographic imaging system, an imaging device is placed on the tube end to take photos of the inner surface of tube and the outer surface of tube including the tube-to-tubesheet weld. These photos are analyzed any suspected damage area, then displaying the location of the area on a computer screen using system software. In the periodic inspections at site, the presence or absence of a local corrosion dent or other damage are determined by visual testing and finger touch, but it is concerned that hundreds of welds will not be covered by these methods sufficiently. Consequently, this new method enables us to eliminate lacks of inspection and the secular change of each weld will be easily grasped with the suitable records. The above testing systems perform in a short amount of time and are highly reliable methods with inspection records. For this reason, in particular, the application of the new method to a periodic inspection at site is expected to reduce the period until the plant resumes operations, and also to remove any concerns about emergency shutdown of an operating plant due to damages in the tube-to-tubesheet welds.

PC-based real-time defect imaging system for high-speed web inspection

1993 ◽  
Author(s):  
James W. Roberts ◽  
S. D. Rose ◽  
Graham A. Jullien ◽  
Lee T. Nichols ◽  
P. Tom Jenkins ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Imaging System ◽  
Web Inspection ◽  
Defect Imaging

scholarly journals Development of 360-degree imaging system for fresh fruit bunch (FFB) identification

2021 ◽  
Vol 1 (4) ◽  
pp. 1-9
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Imaging System ◽  
Fresh Fruit ◽  
Physical Contact ◽  
New Method ◽  
Coefficient Of Determination ◽  
Palm Tree ◽  
Fresh Fruit Bunch ◽  
Video Acquisition

In every cycle of harvesting operation, farmer does not have any information on how many bunches and which oil palm tree will be harvested. By introducing the 360ᵒ camera imaging system, number of Fresh Fruit Bunch (FFB) can be determined for every tree in a plantation area. Black bunch census was done manually to estimate yield. This was improved by video acquisition using a high resolution 360ᵒ camera integrated with an image processing software for video image processing to calculate number of FFB. Based on the standard planting pattern, it is time consuming process to circle each tree to acquire the 360ᵒ view of each tree. Current technology to approach bunches is destructive and conventional since the process involve physical contact between workers and FFB. Thus, a new method was established by the execution of All-Terrain Vehicle (ATV) between rows in plantation area for video acquisition. Images were extracted and threshold by using MATLAB software. L*, a*, and b* color space was used for the bunch identification throughout 90 samples of images to identify the mean intensity value. Model threshold verification for another 48 samples of images resulted with Coefficient of Determination, R2 of 0.8029 for bunch identification. As a result, a new method for video acquisition was established as well as processing method for bunch identification for large scale plantation area.

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