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

Neaz, Ahmed; Lee, Eun Ha; Jin, Tae Hwan; Cho, Kyung Chul; Nam, Kwangwoo

doi:10.3390/s23125494

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The variable k denotes the elastic stiffness constant of the piezoelectric substrate. This constant embodies the non-zero pressure coefficients associated with the elastic properties, piezoelectric characteristics, and the dielectric constant of the material [19]. In addition, we know from the SAW theory that…”

Section: Principle Of the Saw Micro-force Sensormentioning

confidence: 99%

Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors

Feng,

Yu,

Liu

et al. 2024

Micromachines

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Improving measurement accuracy is the core issue with surface acoustic wave (SAW) micro-force sensors. An electrode transducer can stimulate not only the SAW but also the bulk acoustic wave (BAW). A portion of the BAW can be picked up by the receiving transducer, leading to an unwanted or spurious signal. This can harm the device’s frequency response characteristics, thereby potentially reducing the precision of the micro-force sensor’s measurements. This paper examines the influence of anisotropy on wave propagation, and it also performs a phase-matching analysis between interdigital transducers (IDTs) and bulk waves. Two solutions are shown to reduce the influence of BAW for SAW micro sensors, which are arranged with acoustic absorbers at the ends of the substrate and in grooving in the piezoelectric substrate. Three different types of sensors were manufactured, and the test results showed that the sidelobes of the SAW micro-force sensor could be effectively inhibited (3.32 dB), thereby enhancing the sensitivity and performance of sensor detection. The SAW micro-force sensor manufactured using the new process was tested and the following results were obtained: the center frequency was 59.83 MHz, the fractional bandwidth was 1.33%, the range was 0–1000 mN, the linearity was 1.02%, the hysteresis was 0.59%, the repeatability was 1.11%, and the accuracy was 1.34%.

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Section: Principle Of the Saw Micro-force Sensormentioning

confidence: 99%

Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors

Feng,

Yu,

Liu

et al. 2024

Micromachines

View full text Add to dashboard Cite

show abstract

“…The important content of sensor networks research is the state estimation of filter output. Common information fusion algorithms applied to state estimation in multi-sensor networks including weighted averaging [4,5], multi-Bayesian estimation methods [6,7], Kalman filtering (KF) [8,9].…”

Section: Introduction 1motivation and Related Workmentioning

confidence: 99%

Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight

Yu,

Dai,

et al. 2023

Sensors

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Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests.

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Optimization Method for Denoising Yarn Tension Signals Based on Empirical Wavelet Transform and Adaptive Wavelet Threshold Denoising

Peng,

Hou,

et al. 2023

Preprint

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In the weaving process, yarn tension signals are adversely affected by a considerable amount of uncertain noise sequences, compromising the closed-loop control accuracy of yarn tension. Particularly challenging is the effective preservation of these features when confronted with sudden changes in yarn tension characteristics. To address this issue, we propose an Adaptive Wavelet Threshold Denoising (WTD) optimization method for yarn tension signals based on Empirical Wavelet Transform (EWT). The application of EWT decomposes yarn tension signals into components of different frequencies and scales, with wavelet thresholding used for threshold processing of the decomposed signals. The effectiveness of the proposed method is validated through simulation experiments and on-site data analysis. Results indicate that, compared to the PSO-VMD method and the FastICA method, the SNR after processing with the proposed method is improved by 8.55% and 26.29%, respectively. Root Mean Square Error (RMSE) shows that the denoising result curve of this method fits the simulated data curve more closely, and the sudden changes in the signal characteristics are better preserved. Experimental data verification demonstrates the superior performance of the proposed method in denoising tension signals with three different characteristics, with the SNR being maximally improved by 5.32dB while fully preserving the sudden changes in the signal. The proposed method exhibits excellent denoising effects in experiments on yarn tension signals collected at different speeds on a circular winding machine, with a maximum SNR improvement of 5.29dB. It adapts well to the changes in signals that occur under different operating conditions. This method provides a feasible solution for improving the stability and production efficiency of yarn tension in knitting systems.

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

Cited by 3 publications

References 23 publications

Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors

Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors

Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight

Optimization Method for Denoising Yarn Tension Signals Based on Empirical Wavelet Transform and Adaptive Wavelet Threshold Denoising

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