A temperature compensation method for explosion static pressure in finite space

ZHANG Long; ZOU Hong; ZHANG Baoguo; ZHANG Jijun; ZHANG Dongliang; KONG Deqian

doi:10.11883/bzycj-2019-0234

Volume 40 Issue 3

Mar. 2020

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Article Navigation > Explosion And Shock Waves > 2020 > 40(3): 034102

ZHANG Long, ZOU Hong, ZHANG Baoguo, ZHANG Jijun, ZHANG Dongliang, KONG Deqian. A temperature compensation method for explosion static pressure in finite space[J]. Explosion And Shock Waves, 2020, 40(3): 034102. doi: 10.11883/bzycj-2019-0234

Citation:

ZHANG Long, ZOU Hong, ZHANG Baoguo, ZHANG Jijun, ZHANG Dongliang, KONG Deqian. A temperature compensation method for explosion static pressure in finite space[J]. Explosion And Shock Waves, 2020, 40(3): 034102. doi: 10.11883/bzycj-2019-0234

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A temperature compensation method for explosion static pressure in finite space

doi: 10.11883/bzycj-2019-0234

Northwest Institute of Nuclear Technology, Xi’an 710024, Shaanxi, China

Received Date: 2019-06-12
Rev Recd Date: 2019-08-17
Publish Date: 2020-03-01

Abstract

Abstract

To improve the temperature drift characteristics of piezoresistive pressure sensors, a temperature compensation model for the pressure sensors was constructed based on genetic algorithm and wavelet neural networks. By considering the problems of slow convergence and high probability of the local optimal solutions of the wavelet neural networks, the genetic algorithm was applied to optimize the connection weights, expansion parameters and translation parameters of the wavelet neural networks. Based on the calibration data of the pressure sensors, the BP neural network, wavelet neural network and genetic wavelet neural network were used to study the temperature compensation, respectively. The results show that the genetic wavelet neural network was compatible with the time-frequency local characteristics of the wavelet analysis and the self-learning ability of the neural networks, showing high convergence speed and compensation accuracy. After the compensation, the output values of the sensors were closer to the calibration ones. The maximum error was changed from −17.44 kPa to 0.38 kPa, and the maximum relative error was changed from −14.0% to 0.38%. The constructed model is applied in the temperature compensation of explosion static pressure in finite space, and the practical effect is good.
- explosion static pressure,
- piezoresistive pressure sensor,
- temperature compensation,
- genetic wavelet neural network

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References

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