School of Automation and Software Engineering, Shanxi University, Taiyuan, Shanxi, China.
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China.
PLoS One. 2022 Feb 25;17(2):e0259810. doi: 10.1371/journal.pone.0259810. eCollection 2022.
To meet the high thickness accuracy requirements in cold-rolling processes, a roll eccentricity signal extraction method based on modified particle swarm optimization and wavelet threshold denoising (MPSO-WTD) with intrinsic time-scale decomposition (ITD) is proposed. The strong denoising ability of the wavelet is combined with the decomposition and recognition attributes of ITD for non-stationary signals. Periodic disturbances in strip thickness caused by roll eccentricity are actively compensated. First, the wavelet is used to denoise the signal and the MPSO algorithm is applied to determine a rational threshold and improve the calculation efficiency. Then, the denoised signal is decomposed into proper rotational components (PRCs) using the ITD method, and an appropriate PRC component representing the eccentricity signal is extracted. Finally, the eccentricity compensation signal is applied in the automatic gauge control (AGC) system of the cold rolling mill. During the rolling process, the rolling speed is not constant and will directly affect the frequency of the roll eccentricity signal. To solve this problem, an encoder is installed at the end of the roll and the compensation frequency of the roller eccentricity signal is determined in the roller eccentricity compensation system according to the pulse number output. The results of simulations and experiments show that roll eccentricity signals extracted using the proposed method can effectively remove the influence of interference signals. An average improvement of 62.3% in the roll eccentricity compensation effect was achieved under the stable rolling condition in the finishing rolling stage.
为满足冷轧过程中对厚度精度的高要求，提出了一种基于改进粒子群优化和小波阈值去噪（MPSO-WTD）与固有时间尺度分解（ITD）的轧辊偏心信号提取方法。该方法结合了小波的强去噪能力和 ITD 的分解和识别属性，用于处理非平稳信号。主动补偿由轧辊偏心引起的带材厚度周期性干扰。首先，使用小波对信号进行去噪，并应用 MPSO 算法确定合理的阈值，以提高计算效率。然后，使用 ITD 方法将去噪后的信号分解为适当的旋转分量（PRC），并提取表示偏心信号的适当 PRC 分量。最后，将偏心补偿信号应用于冷轧机的自动厚度控制（AGC）系统。在轧制过程中，轧制速度不是恒定的，这将直接影响轧辊偏心信号的频率。为了解决这个问题，在轧辊的末端安装一个编码器，并根据输出的脉冲数在轧辊偏心补偿系统中确定轧辊偏心信号的补偿频率。仿真和实验结果表明，该方法提取的轧辊偏心信号能够有效去除干扰信号的影响。在精轧阶段稳定轧制条件下，轧辊偏心补偿效果平均提高了 62.3%。
