# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
###### Abstract
As a simple and effective method in digital signal processing, FFT and the improved FFT-based algorithms have been applied for the detection of eccentricity signals [3]. Due to a low resolution, FFT is only applicable when the sampling period is an integer multiple of the eccentricity signal components; however, this condition cannot be satisfied in the rolling process. In this case, spectral leakage and fence effect will occur. Compared with FFT, wavelet transform has good localization characteristics in the time and frequency domains, and some studies used wavelet analysis to extract roll eccentricity signals [4; 5]. However, since the performance of wavelet analysis depends on the selection of the wavelet basis function, frequency overlap tends to occur when detecting similar harmonic frequencies and there is a high calculation amount with large redundancy. [PERSON] et al. [6] proposed a neural network-based method for processing eccentricity signals. However, the neural network has a low resolution and a long learning time and is easy to fall into local minima, resulting in low performance in distinguishing similar frequencies. In recent years, some new methods have been applied to eccentricity signal detection. [PERSON] et al. [7] also used the RBF neural network to extract the roll eccentricity signal and, by using POS to optimize the parameters of the RBF, the accuracy and speed were improved compared with the traditional neural network method. [PERSON] et al. [8] proposed an ensemble empirical mode decomposition (EEMD) method for eccentricity signal extraction. The method can be applied to distinguish similar frequencies in eccentricity signal components, but its performance depends on the amplitude of the adaptive added noise and the selection of the ensemble number in EEMD. In practical applications, it is crucial to properly set the two parameters. [PERSON] et al. [9] also used the EEMD algorithm, but first used the wavelet threshold method to denoise the roll eccentricity disturbance signal. [PERSON] et al. [10] proposed a method for roll eccentricity signal analysis based on the resampling method with computed order tracking, which can solve the problems of frequency overlap and frequency change due to the change in the rolling speed. However, in this method, phase detection signals are obtained by data fitting and original signals cannot be reconstructed, thus it is difficult to guarantee the accuracy of the constructed eccentricity model. [PERSON] et al. [11] proposed a method for detecting roll eccentricity signals based on the high-order cumulant-based Root-MUSIC method and Prony method. The proposed method performed well in frequency resolution and noise immunity and the accuracy of the reconstructed eccentricity model was also high. From the perspective of engineering applications of roll eccentricity compensation, [PERSON] et al. [12] developed an adaptive observer to estimate the eccentricity signal frequency for a single supporting roll eccentricity. Then, the amplitude and frequency of eccentricity signals were estimated by an adaptive algorithm based on normalized gradient. [PERSON] and [PERSON] [13] proposed a multiple modulation zoom spectrum analysis for estimating the frequency and the amplitude of roll eccentric harmonics and the phase angle of the roll eccentric harmonics were estimated by FFT. [PERSON] et al. [14] used wavelet analysis that determined a reasonable threshold by MPSO algorithm to first denoise the signal. Then, the roll eccentricity signal was extracted from the denoised signal by decomposition into proper rotational components using the intrinsic time-scale decomposition. Considering the above characteristics of roll eccentricity signals and from the perspective of practical applications, this paper proposed a roll eccentricity detection method by combing the iterative adaptive approach (IAA) and the sparse fast Fourier transform (SFFT). IAA is a nonparametric spectral estimation method proposed by [PERSON] in recent years [15].