Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach
| dc.contributor.author | Yang, Hailu | en |
| dc.contributor.author | Yang, Yue | en |
| dc.contributor.author | Hou, Yue | en |
| dc.contributor.author | Liu, Yue | en |
| dc.contributor.author | Liu, Pengfei | en |
| dc.contributor.author | Wang, Linbing | en |
| dc.contributor.author | Ma, Yuedong | en |
| dc.date.accessioned | 2022-03-28T14:03:40Z | en |
| dc.date.available | 2022-03-28T14:03:40Z | en |
| dc.date.issued | 2022-03-20 | en |
| dc.date.updated | 2022-03-24T14:46:42Z | en |
| dc.description.abstract | Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10<sup>−2</sup>/°C to 1.896 × 10<sup>−4</sup>/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Yang, H.; Yang, Y.; Hou, Y.; Liu, Y.; Liu, P.; Wang, L.; Ma, Y. Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach. Sensors 2022, 22, 2396. | en |
| dc.identifier.doi | https://doi.org/10.3390/s22062396 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/109458 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | piezoelectric sensor | en |
| dc.subject | temperature compensation | en |
| dc.subject | GA-BP neural network | en |
| dc.subject | Weigh-In-Motion | en |
| dc.subject | error analysis | en |
| dc.title | Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach | en |
| dc.title.serial | Sensors | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |