Piezoelectric Smart Patch Operated with Machine Learning Algorithms for Effective Detection and Elimination of Condensation

Zhang, Qian, Wang, Yong, Wang, Tao, Li, Dongsheng, Xie, Jin, Torun, Hamdi and Fu, Richard (Yongqing) (2021) Piezoelectric Smart Patch Operated with Machine Learning Algorithms for Effective Detection and Elimination of Condensation. ACS Sensors, 6 (8). pp. 3072-3081. ISSN 2379-3694

ACS_sensor_modified_paper.pdf - Accepted Version

Download (1MB) | Preview
Official URL: https://doi.org/10.1016/10.1021/acssensors.1c01187


Timely detection and elimination of surface condensation is crucial for diverse applications in agriculture, automotive, oil and gas industries, and respiratory monitoring. In this paper, a smart patch based on a ZnO/aluminum (~5 μm/50 μm thick) flexible Lamb wave device has been proposed to detect, prevent and eliminate condensation, which can be realized using both of its surfaces. The patch is operated using a machine learning algorithm which consists of data preprocessing (feature selection and optimization) and model training by a random forest algorithm. It has been tested in six cases, and the results show good detection performance with average Precision = 94.40 and average F1 score = 93.23. Principle of accelerating evaporation is investigated in order to understand the elimination and prevention functions for surface condensation. Results show that both dielectric heating and acoustothermal effect have their contributions, whereas the former is found more dominant. Furthermore, the functional relationship between the evaporation rate and the input power is calibrated, showing a high linearity (R2 = 97.64) with a slope of ~3.6×10-5 1/(s·mW). With an input power of ~0.6 W, the flexible device has been proven effective in the prevention of condensation.

Item Type: Article
Uncontrolled Keywords: Flexible devices, Condensation detection and elimination, Lamb waves, Random forest algorithm, Respiration detection
Subjects: G600 Software Engineering
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 04 Aug 2021 08:53
Last Modified: 18 Aug 2022 03:32
URI: http://nrl.northumbria.ac.uk/id/eprint/46840

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics