Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach

dc.contributor.authorYang, Hailuen
dc.contributor.authorYang, Yueen
dc.contributor.authorHou, Yueen
dc.contributor.authorLiu, Yueen
dc.contributor.authorLiu, Pengfeien
dc.contributor.authorWang, Linbingen
dc.contributor.authorMa, Yuedongen
dc.date.accessioned2022-03-28T14:03:40Zen
dc.date.available2022-03-28T14:03:40Zen
dc.date.issued2022-03-20en
dc.date.updated2022-03-24T14:46:42Zen
dc.description.abstractPiezoelectric 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 &times; 10<sup>&minus;2</sup>/&deg;C to 1.896 &times; 10<sup>&minus;4</sup>/&deg;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.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationYang, 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.doihttps://doi.org/10.3390/s22062396en
dc.identifier.urihttp://hdl.handle.net/10919/109458en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectpiezoelectric sensoren
dc.subjecttemperature compensationen
dc.subjectGA-BP neural networken
dc.subjectWeigh-In-Motionen
dc.subjecterror analysisen
dc.titleInvestigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approachen
dc.title.serialSensorsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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