Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA

Abstract

The roll eccentricity signal is a weak and complex periodic signal that is difficult to be identified. To improve the detection accuracy of the roll eccentricity signal and to compensate effectively, this study proposed a roll eccentricity signal detection method by combining the sparse fast Fourier transform (SFFT) and the iterative adaptive approach (IAA). The proposed method can rapidly determine the frequency range of the roll eccentricity signal by using the SFFT. Then, it divides the frequency range into several small frequency bands. In each small frequency band, the frequency, amplitude, and phase angle of each harmonic component of the roll eccentricity signal were estimated by using IAA. The simulation results show that the proposed method can find all frequency components, and the frequency estimation accuracy is higher than 99.88%. Finally, the engineering application of this method and the eccentricity compensation control were investigated. In engineering applications, the proposed method can reduce the thickness fluctuation of the finished strip by 89.2%, and the product quality is improved significantly. The simulation results and engineering experiments show that the proposed method has an excellent effect on detecting and compensating roll eccentricity signals.

Roll Eccentricity Detection and Application Based on SFT and Regional DFT

“…To achieve rapid identification of the source and a mathematical model of roll eccentricity signals, this paper proposes a method for identifying and detecting roll eccentricity signals using sparse Fourier transform (SFT) and regional DFT. The application of this method and roll eccentricity compensation control in practical engineering was studied through experiments [ 1 ]. The innovation of this method is that it makes full use of the rapidity of SFT and the regioselectivity of DFT.…”

“…Usually, the eccentricity signal of the roll is analyzed and extracted from data such as roll gap, thickness, or rolling force, and the mathematical model for obtaining the eccentricity of the roll is identified. Based on this mathematical model, the eccentricity signal is specifically calculated and compensated in reverse through the hydraulic servo system of the rolling mill [ 1 ]. This chapter mainly explores and studies how to identify and obtain eccentric disturbance models.…”

“…The eccentric disturbance of the rolling mill roll can cause high-frequency periodic changes in the roll gap during actual operation, resulting in a certain periodic deviation in the outlet thickness of the strip. The thickness of the strip outlet is an important indicator for measuring the quality of the plate and strip steel [ 1 ], and its value plays a decisive role in the quality of the steel plate. In the actual production line of strip, it is necessary to quickly and accurately analyze the information on roll eccentricity disturbance and reverse-compensate it into the AGC control system, in order to solve the interference problem of eccentricity disturbance more efficiently and accurately and achieve the goal of improving the accuracy of strip outlet thickness.…”

“…The eccentricity disturbance of the roller is caused by the irregularity of the shape of the roller and the roller bearing, which mainly occurs during manufacturing, grinding, thermal expansion, and other processes, resulting in the non-circularity of the roller itself and the separation of the geometric axis of the roller body from the rotational axis. The eccentric disturbance of the rolling rolls causes high-frequency periodic changes in the actual gap of the rolling mill, resulting in periodic deviations in the thickness of the strip outlet [ 1 ]. The eccentricity disturbance of the rolling roller causes a deviation in the thickness of the strip outlet, while also reducing the adjustment quality of the conventional regulator, affecting the thickness value of the strip and causing fluctuations.…”

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

“…Reference [11] introduced a dynamic compensation feature in the Regularized Generalized Orthogonal Matching Pursuit algorithm, which identifies and suppresses resonant noise based on the signal's priori features and directly reconstructs denoised signals from subsampled measurements of the original signal. Reference [12] presented a signal compensation method that combines the Sparse Fast Fourier Transform (SFFT) and the Iterative Adaptive Approach (IAA). Reference [13] offered a roller eccentricity signal compensation method based on improved Particle Swarm Optimization and wavelet threshold denoising.…”