Affiliation:
1. School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
2. School of Engineering, Hangzhou Normal University, Hangzhou 311121, China
Abstract
In this paper, a SAW winding tension sensor is designed and data fusion technology is used to improve its measurement accuracy. To design a high-measurement precision SAW winding tension sensor, the unbalanced split-electrode interdigital transducers (IDTs) were used to design the input IDTs and output IDTs, and the electrode-overlap envelope was adopted to design the input IDT. To improve the measurement accuracy of the sensor, the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm was used to compensate for the temperature error. After temperature compensation, the sensitivity temperature coefficient αs of the SAW winding tension sensor was decreased by an order of magnitude, thus significantly improving its measurement accuracy. Finally, the error with actually applied tension was calculated, the same in the LSSVM and PSO-LSSVM. By multiple comparisons of the same sample data set overall, as well as the local accuracy of the forecasted results, which is 5.95%, it is easy to confirm that the output error predicted by the PSO-LSSVM model is 0.50%, much smaller relative to the LSSVM’s 1.42%. As a result, a new way for performing data analysis of the SAW winding tension sensor is provided.
Funder
Zhejiang Provincial Natural Science Foundation of China
Scientific Research Fund of Zhejiang Provincial Education Department
Scientific Research Fund of Hangzhou Normal University
Joint Funds of the Zhejiang Provincial Natural Science Foundation of China
“The Professional Development Projects of Teachers” for Domestic Visiting Scholars of Colleges and Universities in 2022
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
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