# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
## 6 Conclusions  
This study focused on roll eccentricity signal detection and eccentricity compensation, which is key to improving the quality of finished strip steels. In this study, a roll eccentricity signal detection method was proposed based on SEFT-IAA, and the applications of the proposed method and the roll eccentricity compensation and control in practical engineering were investigated through experiments. The method proposed has not been reported in the field of roll eccentricity compensation, especially focusing on estimating similar  
Figure 14: Eccentricity compensation model.  
Figure 15: The control effect of eccentricity compensation.  
frequency components in the eccentricity signal. The innovation of the proposed method is to improve the IAA, which makes full use of the rapidity of SFFT and the accuracy of the IAA algorithm; the accuracy and speed of the roll eccentricity signal detection are improved significantly. The proposed method has no particularly complicated calculation steps. It is easy to be implemented by computer programs and suitable for engineering applications. In the simulation experiments, the proposed method can find all eight frequencies of the simulated signal, and the average accuracy of the proposed method is 99.99% and 99.88% when SNR = 30 and SNR = 10, respectively. When this method was applied in engineering experiments, the thickness fluctuation of exporting strip steels was reduced by 89.2% and the product quality was significantly improved, indicating a high application value in engineering applications. Simulation and practical engineering experiments revealed that the proposed method has a fast computational speed and a strong anti-interference ability and it can distinguish the harmonic components with very similar frequencies in the roll eccentricity signal, thus the proposed method can be used to establish a highly accurate roll eccentricity model. In the study, limitations of the proposed method were also found. As the SNR decreases, the accuracy of the IAA step of the proposed method decreases significantly and satisfactory results cannot be obtained. Subsequent work will further investigate to improve the anti-noise performance of the proposed method. At the same time, the proposed method has many calculation steps, and the proposed algorithm has many calculation steps; therefore, reducing the complexity of the algorithm is also the content of further research.  
Conceptualization, Z.Y. and G.Z.; methodology, Z.Y.; software, Z.Y.; validation, Z.Y. and G.Z.; formal analysis, D.L. and Z.Y.; investigation, Z.Y.; resources, Z.Y.; data curation, G.Z.; writing--original draft preparation, Z.Y.; writing--review and editing, D.L.; visualization, Z.Y.; supervision, D.L. and G.Z.; project administration, D.L. and G.Z.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation (NNSF) of China [grant numbers 62003261 and 62073258].  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.