Methods of solving in‐band ripples and out‐of‐band suppression for yarn tension sensor based on surface acoustic wave

Feng, Yang; Li, Jun; Bai, Ru; Qian, Zhenghong

doi:10.1049/cds2.12121

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…To suppress the sidelobes, we adopted the unbalanced splitelectrode interdigital transducers, namely, in Figure 2(b), within each 𝜆 length period, each electrode of the input and output IDT is split into two electrodes by 1:3 according to its width a, and then the split-electrodes are arranged at equal intervals. By adopting this structure, the phase of the marginal reflection is close to 180 degrees so that most of the marginal reflection can be offset near the center frequency [19].…”

Section: Principle Of Temperature Compensation For Saw Winding Tensio...mentioning

confidence: 99%

“…The surface acoustic wave (SAW) device has been widely used in mechanical engineering, aeronautics and astronautics, signal processing [13], nondestructive testing, sensor technology, and so on [14], and as important sensing technologies, SAW sensors have exhibited promising characteristics because of their high sensitivity, fast response, excellent specificity, reversibility, battery-powered operation, small size, and low cost, thus offering extensive potential use in the future [15]. The SAW sensors can be used to measure temperature [16], humidity [17], gas [18], force [19], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Temperature Compensation of SAW Winding Tension Sensor Based on PSO-LSSVM Algorithm

Feng,

Liu,

et al. 2023

Micromachines

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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.

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Section: Principle Of Temperature Compensation For Saw Winding Tensio...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of SAW Winding Tension Sensor Based on PSO-LSSVM Algorithm

Feng,

Liu,

et al. 2023

Micromachines

Self Cite

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“…(2) Improvement of the winding tension control system. Feng et al 24 designed a surface acoustic wave (SAW) yarn tension sensor. Multiple yarns can be detected quickly with the sensor, which overcomes the problems of the slow response speed and low resolution of traditional sensors.…”

mentioning

confidence: 99%

Active tension control of the conical winding system based on the neural network control algorithm of radial-basis functions

Zhang

Wang

Wu³

et al. 2023

Textile Research Journal

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A conical winding formation and tension control system was proposed in the doubling operation based on the yarn guide mode of a single spindle in this study. Conical winding formation realized the radial unwinding of wound package. An overfeed mechanism was introduced to achieve closed-loop control of yarn tension. The overfeed wheel was driven by a brushless Direct Current motor. The tension control system combined a Proportion Integration Differentiation controller with a radial-basis-function neural network, whose purpose was to meet the control requirements of the brushless DC motor. This system consisted of three main steps: Firstly, the radial-basis-function neural network was used to identify the system online. Secondly, the gradient descent method was used to adjust the node weight, center vector, and baseband width. Finally, incremental PID parameters online were adjusted according to the identified Jacobian information. A mathematical model of a control system was established in Matrix Laboratory. An experimental platform was designed for doubling winder to compare the control effects of Radial-basis-function-PID with traditional PID. The simulation results showed that the RBF-PID had a smaller overshoot of yarn tension, shorter adjustment time, and smaller steady-state error compared with the traditional PID controller in doubling operation by simulating the mathematical model. Experimental results showed the RBF-PID controller had good performance and stability and could be applied to yarns with different average linear velocity, yarn counts and strands . The yarn tension fluctuation will not exceed ±3% of the target value when the experimental materials and the cone angles are unchanged.

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Methods of solving in‐band ripples and out‐of‐band suppression for yarn tension sensor based on surface acoustic wave

Cited by 2 publications

References 23 publications

Temperature Compensation of SAW Winding Tension Sensor Based on PSO-LSSVM Algorithm

Temperature Compensation of SAW Winding Tension Sensor Based on PSO-LSSVM Algorithm

Active tension control of the conical winding system based on the neural network control algorithm of radial-basis functions

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