Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements

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 OPEN ACCESSMicromachines 2015, 6 555 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.

Selected Aging Effects in Triaxial MEMS Accelerometers

“…Since the observed changes due to aging related to the offset and the scale factor are of considerable values (and would be definitely bigger under harsher conditions of storing and operation), it is advantageous to repeatedly calibrate the accelerometers. Then, the related errors can be compensated for [11], to some extent of course. Many different calibration procedures have been successfully used so far, some typical, as, e.g., in [43], some quite sophisticated, as, e.g., in [8-10, 44, 45].…”

“…In order to obtain possibly high accuracy, the output signals generated by MEMS accelerometers are often calibrated in very sophisticated ways, as reported, e.g., in [8][9][10]. Then, at the same time, the aging phenomena are compensated for [11]. However, if calibration was not repeated within a longer period of time, it would be advantageous to take into account errors due to aging and compensate for them.…”

A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction

“…It is network framework is the topological structure of BP neural network, and it takes the wavelet basis function as the hidden layer excitation function [21]. WNN is combined with the time-frequency domain local properties of wavelet analysis and the self-learning, self-adaptive ability of neural network, so WNN has better generalization ability than neural network [22][23][24]. Wavelet neural network can be divided into two types: loose wavelet neural network and tight wavelet neural network [25].…”

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

“…Analog circuits are nonlinear systems with continuous working signals, and consecutive input compression is very complex. Maintaining the same precision and recall index for compression tests is challenging because compression simulation testing may increase the false alarm rate and cause unnecessary losses [5]. Therefore, the test compression algorithm should guarantee the functional equivalency as well as the consistent performance under the original test and compression test.…”