Geometry Factor Determination for Tetrapolar Impedance Sensor Probes

Author(s):  
Carina Veil ◽  
Raphael Bach ◽  
Peter Somers ◽  
Oliver Sawodny ◽  
Cristina Tarin
Author(s):  
Н. Г. Крылова ◽  
Г. В. Грушевская ◽  
И. В. Липневич ◽  
Т. И. Ореховская ◽  
Г. Н. Семенкова ◽  
...  

2015 ◽  
Vol 68 ◽  
pp. 577-585 ◽  
Author(s):  
Hamed Abiri ◽  
Mohammad Abdolahad ◽  
Milad Gharooni ◽  
Seyed Ali Hosseini ◽  
Mohsen Janmaleki ◽  
...  

Author(s):  
Kanika Dheman ◽  
Philipp Mayer ◽  
Manuel Eggimann ◽  
Simone Schuerle ◽  
Michele Magno
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4456
Author(s):  
Sungjae Ha ◽  
Dongwoo Lee ◽  
Hoijun Kim ◽  
Soonchul Kwon ◽  
EungJo Kim ◽  
...  

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.


2018 ◽  
Vol 1144 ◽  
pp. 012180
Author(s):  
K Masaen ◽  
J Sanglao ◽  
P Chimsiri ◽  
R Techapiesancharoenkij ◽  
N Pussadee

1970 ◽  
Vol 9 (5) ◽  
pp. 401-404 ◽  
Author(s):  
G.M. Reimer ◽  
D. Storzer ◽  
G.A. Wagner

2013 ◽  
Vol 178 ◽  
pp. 310-315 ◽  
Author(s):  
Ting Yang ◽  
Shun Wang ◽  
Huile Jin ◽  
Weiwei Bao ◽  
Shaoming Huang ◽  
...  

2021 ◽  
Author(s):  
Chuan Yu ◽  
Qinghai Yang ◽  
Songbo Wei ◽  
Ming Li ◽  
Tao Fu

Abstract Single-layer water cut measurement is of great significance for identifying and shutting off the unwanted water, analyzing oil remained and optimizing production. Currently, however, only the water cut of multilayer mixture can be measured by testing samples taken from wellhead, a way which is widely used in oilfields. That of single-layer fluid cannot be determined yet To address the problem, this paper puts forward a new impedance sensor that offers long-term online monitoring of single-layer water cut. This sensor is based on the different electrical conductivity of oil and water. It has two layers. The inner one contains three electrodes - two at both sides sending sinusoidal excitation signals and one at the middle receiving signals that have been attenuated by the water-oil medium. With the Maxwell's model of oil-water mixed fluid, the receiver then can measure the water cut online. The outer layer of the sensor is made of PEEK, an insulative protection. In front of the electrodes lies a static mixer which makes the measurement more accurate by fully blending the two media when they flow through the electrodes. Laboratory tests are carried out with the prototype of the sensor at various oil-water mixing ratios, fluid flow rates, and temperatures. Results show that the average margin of error is within ± 3%. Higher accuracy is seen when high water cut and flow rate enable oil globules to disperse more evenly and the space in between to get wider and the RMS error is less than 2%. If the water cut drops below 80%, the aggregation of the droplets will cause wild fluctuation and more errors in the measurement. In addition, the mineralization of the mixture directly changes its conductivity, which largely impacts the result. Meanwhile, temperature can influence the ionic movement intensity and then alter the conductivity of the medium. Therefore, in practice, the sensor calibration needs to be performed according to the range of medium salinity, and the temperature of the medium is collected in real time for temperature compensation. It is shown that after the adjustment, the water cut measurement results have higher accuracy and consistency. The impedance sensor can realize online water cut monitoring for a single-layer, indicated by tests. It is more suitable for the increasing high water cut oilfields in that it is more accurate as the water cut grows.


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