Abstract
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.
Highlights
In strip production, aluminum alloy strip quality is one of the most important factors affecting consumer selection when deciding among similar competing products
To improve roll eccentricity signal extraction, this paper proposes an modified particle swarm optimization (MPSO)-WTD method with intrinsic time-scale decomposition (ITD)
A roll eccentricity extraction method based on MPSO-TWD and ITD was proposed to improve the accuracy of roll eccentricity signal extraction
Summary
Aluminum alloy strip quality is one of the most important factors affecting consumer selection when deciding among similar competing products. The Fourier transform is widely used to process linear stationary signals and can be used to effectively analyze the frequency characteristics of signals. The wavelet threshold denoising method can conveniently and flexibly extract roll eccentricity signals. An eccentricity signal extraction method combining improved wavelet denoising and ensemble empirical mode decomposition (EEMD) was previously proposed [3]. The EEMD method can suppress the frequency aliasing phenomenon of the wavelet algorithm and improve the eccentric signal extraction accuracy. To improve the accuracy of roller eccentricity signal compensation, the influence of rolling speed and roll wear on eccentricity signal should be considered. To verify the effect of the eccentricity compensation signal on improving strip thickness characteristics, compensation signals were input into the AGC system of a four-high irreversible cold strip rolling mill
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