Empirical mode decomposition-adaptive least squares method for dynamic calibration of pressure sensors

2017 ◽  
Vol 28 (4) ◽  
pp. 045010 ◽  
Author(s):  
Zhenjian Yao ◽  
Zhongyu Wang ◽  
Jeffrey Yi-Lin Forrest ◽  
Qiyue Wang ◽  
Jing Lv
Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1052 ◽  
Author(s):  
Fujing Xu ◽  
Tiehua Ma

Transient pressure testing is often accompanied by shock acceleration. Aiming at the acceleration-induced effects of pressure sensors, a dynamic compensation method combining empirical mode decomposition (EMD) with system identification theory (SIT) is proposed in this paper. This method is more effective at reducing the error of the acceleration-induced effects without affecting the sensor’s sensitivity and inherent frequency. The principle and theoretical basis of acceleration-induced effects is analyzed, and the static and dynamic acceleration-induced effects on the quartz crystal of a piezoelectric pressure sensor are performed. An acceleration-induced effects dynamic calibration system is built using a Machete hammer, which generates acceleration signals with larger amplitude and narrower pulse width, and an autoregressive exogenous (ARX)mathematical model of acceleration-induced effects is obtained using empirical mode decomposition-system identification theory (EMD-SIT). A digital compensation filter for acceleration-induced effects is designed on the basis of this model. Experimental results explain that the acceleration-induced effects of the pressure sensor were less than 11% after using the digital compensation filter. A series of test data verify the accuracy, reliability, and generality of the model.


2017 ◽  
Vol 34 (7) ◽  
pp. 1519-1528 ◽  
Author(s):  
Roger J. Laurence ◽  
Brian M. Argrow ◽  
Eric W. Frew

AbstractThe multihole probe (MHP) is an effective instrument for relative wind measurements from small unmanned aircraft systems (sUAS). Two common drawbacks for the integration of commercial MHP systems into low-cost sUAS are that 1) the MHP airdata system cost can be several times that of the sUAS airframe; and 2) when extended from the airframe, the pressure-measuring probe is often exposed to damage during normal operations. A flush airdata system (FADS) with static pressure sensing ports mounted flush with the airframe skin provides an alternative to the MHP system. This project implements a FADS with multiple static pressure sensors located at selected locations on the airframe. Computational fluid dynamics simulations are used to determine the airframe locations with the highest pressure change sensitivity to changes in the airframe angle of attack and sideslip angle. Wind tunnel test results are reported with nonlinear least squares and neural networks regression methods applied to the pressure measurements to estimate the instantaneous angle of attack and sideslip. Both methods achieved mean errors of less than . A direct comparison of the regression methods show that the neural network method provides a more accurate relative wind angle estimate than the nonlinear least squares method.


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