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Open AccessArticle

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

by 1,2,* and 1,2
1
School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
2
Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(8), 2397; https://doi.org/10.3390/s20082397
Received: 9 March 2020 / Revised: 15 April 2020 / Accepted: 21 April 2020 / Published: 23 April 2020
(This article belongs to the Special Issue Piezoelectric Transducers)
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. View Full-Text
Keywords: piezoelectric pressure sensor (PPS), stress wave effect (SWE), split Hopkinson pressure bar (SHPB), physical isolation; digital compensation; artificial neural network (ANN) piezoelectric pressure sensor (PPS), stress wave effect (SWE), split Hopkinson pressure bar (SHPB), physical isolation; digital compensation; artificial neural network (ANN)
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Feng, L.; Ma, T. 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, 20, 2397.

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