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.
We designed a semi-real simulation platform for the cold rolling mill that was composed of Simulation Computer, Simulation System of Field Signal, AGC Control System, Console of Rolling Mill and the Main Control Computer, and built the mathematical models in the Simulation Comuter, for the hydraulic servo system, rolling force and thickness of strip material. In the platform, the Simulation System of Field Signal output the simulation data as standard sensor signal; the AGC Control System is the same as that used in real rolling mill system. We simulated the milling process of real rolling mill system at this platform using the field-recorded online data. The differences between simulation results and those in real system were small. Simulation results show that this simulation platform can be used in the research and optimization of rolling mill control system.
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