Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements
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 appli...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-05-01
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| Series: | Micromachines |
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| Online Access: | http://www.mdpi.com/2072-666X/6/5/554 |
| _version_ | 1811276291583770624 |
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| author | Jiahong Zhang Yusheng Wu Qingquan Liu Fang Gu Xiaoli Mao Min Li |
| author_facet | Jiahong Zhang Yusheng Wu Qingquan Liu Fang Gu Xiaoli Mao Min Li |
| author_sort | Jiahong Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T23:54:57Z |
| format | Article |
| id | doaj.art-8a263089f06f490990eb6cc21078626b |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-04-12T23:54:57Z |
| publishDate | 2015-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-8a263089f06f490990eb6cc21078626b2022-12-22T03:11:34ZengMDPI AGMicromachines2072-666X2015-05-016555457310.3390/mi6050554mi6050554Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological MeasurementsJiahong Zhang0Yusheng Wu1Qingquan Liu2Fang Gu3Xiaoli Mao4Min Li5Jiangsu Key Laboratory of Meteorological Observation and information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJiangsu Key Laboratory of Meteorological Observation and information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJiangsu Key Laboratory of Meteorological Observation and information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJiangsu Key Laboratory of Meteorological Observation and information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaThis 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.http://www.mdpi.com/2072-666X/6/5/554high-precisionarray of MEMS pressure sensorsdata fusionwavelet neural networkgenetic algorithmtemperature drift compensationhysteresis compensationlong-term stabilityhardware implementation of GA-WNN model |
| spellingShingle | Jiahong Zhang Yusheng Wu Qingquan Liu Fang Gu Xiaoli Mao Min Li Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements Micromachines high-precision array of MEMS pressure sensors data fusion wavelet neural network genetic algorithm temperature drift compensation hysteresis compensation long-term stability hardware implementation of GA-WNN model |
| title | Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements |
| title_full | Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements |
| title_fullStr | Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements |
| title_full_unstemmed | Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements |
| title_short | Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements |
| title_sort | research on high precision low cost piezoresistive mems array pressure transmitters based on genetic wavelet neural networks for meteorological measurements |
| topic | high-precision array of MEMS pressure sensors data fusion wavelet neural network genetic algorithm temperature drift compensation hysteresis compensation long-term stability hardware implementation of GA-WNN model |
| url | http://www.mdpi.com/2072-666X/6/5/554 |
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