Application Of Subspace Decomposition Algorithm In Roll Eccentricity Signal Extraction

Rolling eccentricity is part of the key factors affecting the thickness of strip quality in modern high-precision rolling process. In order to further enhance the strip mill quality of flatness and thickness, an effective compensation control and disturbances rejection in rolling eccentricity must be applied. Due to roll eccentricity signal is mixed in a complex high-frequency periodic signal, in which various disturbances surrounding. The control effect of rolling eccentricity compensation depends on how to accurately extract the weak eccentricity signal from the complex rolling force signal that the accurate signal extraction algorithm is a critical factor in eccentricity compensation control. To this point, a summarization of research on limitations of the existing extraction methods for rolling eccentricity signal is made. And it is proposed that apply subspace decomposition algorithm in the contemporary spatial spectrum estimation to solve the rolling eccentricity signal extraction problem is validity, and as well as related integration and improvement of this class of algorithm is given. Through a combination of theoretical analysis, simulation and experimental study to validate the efficiency and accuracy of this signal extraction algorithm, especially with this algorithm, the performances of the frequency resolution and noise suppression are much better than the FFT method. The main innovations in this research thesis are as follows:First,for the limitations of low frequency resolution and poor noise-canceling effect of the FFT method in rolling eccentricity extraction, it is proposed that the improved version of noise subspace MUSIC algorithm apply to the eccentricity signal extraction. From the aspect of technological innovation for application, focusing on a novel extraction algorithm based on the fusion of Prony and Root-MUSIC methods. Simulation results show the effectiveness of the new algorithm combining of high resolution, robust noise immunity.Second,in order to decrease the computational burden, to improve the online timeliness, and to ease of application in actual industrial tasks. This is such an original idea that an improved subspace ESPRIT algorithm is applied to roll eccentricity signal extraction. The subspace ESPRIT algorithm has more usability compared to the MUSIC method, for it no longer examines the relationship between the signal subspace and noise subspace, no spectral peak searching. Simulation for analysing the frequency estimation performance of three different ESPRIT methods is given under different number of array elements and different SNR.Third,to the characteristics of rolling eccentricity signal that the measurement is non-stationary and colored noise is actually existed, and the nature of classical algorithm of second-order statistics in quantum space has the problems of bias and non-uniformity. So the integrating method with both higher-order cumulant MUSIC and Prony is used to eccentricity signal extraction. Based on this method, spatial decomposition of eccentricity achieves reduced-order effect. This method still has a high spectral resolution and can accurately extract the frequencies of the eccentric harmonics and the number of the harmonics. Then Prony method is used to estimate the amplitudes and phases of the harmonics, which remedies the weaknesses of sensitive to noise and the demand for the order of the signal. The simulation shows that combining the two ways is effective.Taking a hot-rolled production line in a factory as a background, the proposed method is verified by using the rolling force data, and by the MUSIC algorithm based on HOC and Prony method to extract rolling eccentricity signal. The experimental results demonstrate that the method can accurately extract parameters of similar frequency and high-order harmonics. And the proposed reconstruction eccentric model has higher precision than the FFT method. The eccentricity compensation effect is obvious.