Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN
Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5225 |
| _version_ | 1797433112569839616 |
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| author | Yonglei Shi Liqing Fang Zhanpu Xue Ziyuan Qi |
| author_facet | Yonglei Shi Liqing Fang Zhanpu Xue Ziyuan Qi |
| author_sort | Yonglei Shi |
| collection | DOAJ |
| description | Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s<sup>2</sup> to −1.2646 × 10<sup>−</sup><sup>7</sup> m/s<sup>2</sup>, while the variance decreases from 0.0133 m/s<sup>2</sup> to 1.0975 × 10<sup>−</sup><sup>5</sup> m/s<sup>2</sup>. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals. |
| first_indexed | 2024-03-09T10:12:29Z |
| format | Article |
| id | doaj.art-65bb1b0148b947bbb60d4d6ebebc3023 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:12:29Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-65bb1b0148b947bbb60d4d6ebebc30232023-12-01T22:40:06ZengMDPI AGSensors1424-82202022-07-012214522510.3390/s22145225Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNNYonglei Shi0Liqing Fang1Zhanpu Xue2Ziyuan Qi3Department of Artillery Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaDepartment of Artillery Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaSchool of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaDepartment of Artillery Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaRandom drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s<sup>2</sup> to −1.2646 × 10<sup>−</sup><sup>7</sup> m/s<sup>2</sup>, while the variance decreases from 0.0133 m/s<sup>2</sup> to 1.0975 × 10<sup>−</sup><sup>5</sup> m/s<sup>2</sup>. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.https://www.mdpi.com/1424-8220/22/14/5225MEMS sensorrandom drifterror compensationneural network |
| spellingShingle | Yonglei Shi Liqing Fang Zhanpu Xue Ziyuan Qi Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN Sensors MEMS sensor random drift error compensation neural network |
| title | Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN |
| title_full | Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN |
| title_fullStr | Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN |
| title_full_unstemmed | Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN |
| title_short | Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN |
| title_sort | research on random drift model identification and error compensation method of mems sensor based on eemd grnn |
| topic | MEMS sensor random drift error compensation neural network |
| url | https://www.mdpi.com/1424-8220/22/14/5225 |
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