Abstract
Nowadays, the commonly used yarn tension sensors cannot meet the requirement well in the textile manufacturing process. So surface acoustic wave (SAW) yarn tension sensor is a good selection to adapt to today's textile manufacturing and improve the production efficiency. First, two key problems of SAW yarn tension sensor in practical application are proposed in this article, one is the construction of the sensor measurement system, the other is the temperature compensation. Then, the integrated design of measurement circuit and measurement structure is implemented and introduced in this article, which improves the accuracy, reliability, and sensitivity of SAW yarn tension sensor. Finally, binary regression analysis method and least squares support vector machine (LSSVM) algorithm optimized by particle swarm optimization (PSO-LSSVM) method are applied to the temperature compensation of SAW yarn tension sensor, which both achieve satisfactory compensation effect. By comparison, PSO-LSSVM method is more convenient and flexible, and still has a large space for improvement. Through those research contents, it can be concluded that the design and implementation of the measurement system lay a foundation for the practical application of SAW yarn tension sensor, the research of temperature compensation is of great significance to the wide application of the sensor.
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