Through analyzing the correspondence between the regression function monotonicity and its partial derivative sign, the effect of the SAW yarn tension sensor substrate size on the sensitivity of the SAW yarn tension sensor was investigated.
In order to obtain the best sensitivity, the regression model between the size of the SAW yarn tension sensor substrate and the sensitivity of the SAW yarn tension sensor was established using the least square method.
In addition, we provide a theoretical framework for the design of additional tension sensor modules with adjusted force sensitivity.
An experiment of SAW yarn tension sensor about 15 mm long and 3 mm wide was presented.
In this paper, a new Genetic-neuro-fuzzy hybrid controller without tension sensor has been proposed to optimize the quantum of excessive sag and reduce it.
In this paper, we propose a novel optimal sensitivity design scheme for the yarn tension sensor using surface acoustic wave (SAW) device.
Based on the regression model, a linear programming model was established to gain the optimal sensitivity of the SAW yarn tension sensor.
The input force was measured by a tension sensor and the acceleration of trees was measured by tri-axial acceleration transducers.
The working principle is: based on the conveyor's start-up and loading, with the feedback of tension sensor signal, the PLC controller and fuel tank can automatically adjust the output of tensile force to avoid slipping between belts and drive pulley, lower the safety factor selected for the belt, and ensure the normal operation of the belt conveyor.
The linear programming result shows that the maximum sensitivity will be achieved when the SAW yarn tension sensor substrate length is equal to 15 mm and its width is equal to 3 mm within a fixed interval of the substrate size.
