Abstract
This paper provides a novel and effective compensation method by improving the hardware design and software algorithm to achieve optimization of piezoresistive pressure sensors and corresponding measurement systems in order to measure pressure more accurately and stably, as well as to meet the application requirements of the meteorological industry. Specifically, GE NovaSensor MEMS piezoresistive pressure sensors within a thousandth of accuracy are selected to constitute an array. In the versatile compensation method, the hardware utilizes the array of MEMS pressure sensors to reduce random error caused by sensor creep, and the software adopts the data fusion technique based on the wavelet neural network (WNN) which is improved by genetic algorithm (GA) to analyze the data of sensors for the sake of obtaining accurate and complete information over the wide temperature and pressure ranges. The GA-WNN model is implemented in hardware by using the 32-bit STMicroelectronics (STM32) microcontroller combined with an embedded real-time operating system µC/OS-II to make the output of the array of MEMS sensors be a direct digital readout. The results of calibration and test experiments clearly show that the GA-WNN technique can be effectively applied to minimize the sensor errors due to the temperature drift, the hysteresis effect and the long-term drift because of aging and environmental changes. The maximum error of the low cost piezoresistive MEMS-array pressure transmitter proposed by us is within 0.04% of its full-scale value, and it can satisfy the meteorological pressure measurement.
Highlights
Meteorological disasters are the natural disasters that seriously threaten national security and people’s lives
We apply the MATLAB to establish a data model based on the genetic algorithm (GA)-wavelet neural network (WNN) algorithm and process these training sample data
This shows that the low cost MEMS-array pressure transmitter based on the GA-WNN compensation algorithm can reduce hysteresis effectively and meet the basic requirement of meteorological operations
Summary
Meteorological disasters are the natural disasters that seriously threaten national security and people’s lives. It is worth noting that micromachining manufacturing technology has developed greatly in recent years, and relatively high-precision silicon piezoresistive pressure sensors have been researched and developed [4,5], such as NPC-1210-015A-3L (MEMS pressure sensor from GE NovaSensor, Fremont, CA, USA) which has one-thousandth accuracy and stability at room temperature. In order to improve the measuring precision of the piezoresistive pressure sensor, we have proposed a versatile compensation method based on the array average measurement of MEMS pressure sensors and the data fusion technique using the wavelet neural network optimized by the genetic algorithm (GA-WNN) to mainly solve the nonlinear error, temperature drift and hysteresis error as well as the random error. These measured and corrected temperature and pressure signals are displayed on an LCD screen or transmitted to the host computer by using the serial port
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