Analysis, Modeling and Experimental Study of Stretching Stress in the Design of Pressure Sensor

2021 ◽  
pp. 193-201
Author(s):  
Shrinkhla Ghildiyal ◽  
R. Balasubramaniam ◽  
Joseph John
Keyword(s):  
Experimental Study ◽  
Pressure Sensor

An experimental study on SrB4O7:Sm2+ as a pressure sensor

2013 ◽  
Vol 113 (2) ◽  
pp. 023507 ◽  
Author(s):  
Qiumin Jing ◽  
Qiang Wu ◽  
Lei Liu ◽  
Ji-an Xu ◽  
Yan Bi ◽  
...  
Keyword(s):  
Experimental Study ◽  
Pressure Sensor

Theoretical and experimental study of a monocharged electret for pressure sensor applications

2021 ◽  
Vol 129 (3) ◽  
pp. 034501
Author(s):  
Shaobo Gong ◽  
Jinxi Zhang ◽  
Chenchen Wang ◽  
Kailiang Ren
Keyword(s):  
Experimental Study ◽  
Pressure Sensor ◽  
Sensor Applications

scholarly journals Experimental Study Comparing the Effectiveness of Physical Isolation and ANN Digital Compensation Methodologies at Eliminating the Stress Wave Effect Error on Piezoelectric Pressure Sensor

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2397 ◽  
Author(s):  
Lei Feng ◽  
Tiehua Ma
Keyword(s):  
Experimental Study ◽  
Stress Wave ◽  
Pressure Sensor ◽  
Output Signal ◽  
Polymer Materials ◽  
Wave Effect ◽  
Digital Compensation ◽  
Physical Isolation ◽  
Study Results ◽  
Piezoelectric Pressure Sensor

Stress wave, accompanied by explosion shock wave overpressure measurement and dynamic pressure calibration on shock tube, could cause error signals in the piezoelectric pressure sensor (PPS) used for measuring and calibrating. We may call this error the stress wave effect (SWE). In this paper, the SWE and its isolation from PPS were studied by using a split Hopkinson pressure bar (SHPB). In the experimental study of SWE, when increasing the input stress, the corresponding output signal of the PPS was analyzed, and the existence of SWE was verified using the result of the spectrum analysis of the output signal. The stress wave isolation pedestal used in the stress wave isolation experiment was made of nylon and plexiglass polymer materials. The effects of the isolation pedestal’s materials and length on the stress wave isolation were analyzed using the study results. Finally, an artificial neural network (ANN) was trained with the data of the SWE study and was further applied to compensate the SWE error of the PPS output signal. The compensating results were compared with the isolating results, and the advantages and disadvantages of the digital compensation and physical isolation methods were analyzed.

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