High-Order Polynomial Fitting Assistance for Fast Double-Peak Finding in Brillouin-Distributed Sensing

Soto, Marcelo A.; Jderu, Alin; Dorobantu, Dorel; Enăchescu, Marius; Ziegler, Dominik

doi:10.3390/s21010187

Cited by 1 publication

(1 citation statement)

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“…As shown in Figure 2 , the load sensor introduces a considerable amount of noise [ 16 ] that affects the precision of the control system. A noise filter can alleviate this problem, although it also introduces a trade-off between noise attenuation and phase lag.…”

Section: Signal Processormentioning

confidence: 99%

Optimizing Yarn Tension in Textile Production with Tension–Position Cascade Control Method Using Kalman Filter

Neaz

Lee

Jin

et al. 2023

Sensors

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The production of textiles has undergone a considerable transformation, progressing from its primitive origins in hand-weaving to the implementation of contemporary automated systems. Weaving yarn into fabric is a crucial process in the textile industry that requires meticulous attention to output quality products, particularly in the tension control section. The efficiency of the tension controller in relation to the yarn tension significantly affects the quality of the resulting fabric, as proper tension control leads to strong, uniform, and aesthetically pleasing fabric, while poor tension control can cause defects and yarn breakage, leading to production downtime and increased costs. Maintaining the desired yarn tension during textile production is crucial, although it poses several problems, such as the continuous diameter change of the unwinder and rewinder sections leading to system change. Another problem faced by the industrial operation is maintaining proper tension on the yarn while changing the roll-to-roll operation velocity. In this paper, an optimized method for controlling yarn tension through the cascade control of tension and position, incorporating feedback controllers, feedforward, and disturbance observers, has been proposed to make the system more robust and suitable for industrial use. In addition, an optimum signal processor has been designed to obtain sensor data with reduced noise and minimal phase difference.

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Section: Signal Processormentioning

confidence: 99%