The Data Processing of Single-yarn Strength Testing Based on the Particle Filter Algorithm

Abstract

liuyujiao2005@126.com Abstract Aiming at a nonlinear/non-Gaussian filter problem, the data processing of a single-yarn strength testing system, a filtering method based on the particle filter algorithm is put forward. This paper expounds the principle, and the working process and the procedures of particle filter. It discusses in detail the application of particle filter in the single-yarn strength testing, the modeling of testing system state equation, and the implementation of this algorithm in a project. It also indicates that particle filter algorithm used to process single-yarn strength test data achieves a good effect and satisfactory filtering precision by simulated tests. Finally, this paper predicts that particle filter will play an important role in general data processing and analyzing. The detection of the yarn quality is very important for cotton production. Yarn strength is the reflection of inner quality of yarn and is the necessary condition of yarn's processing performance and end-use. The unit of single-yarn strength is Newton (N) per centimeter (CN). By stretching testing with a single-yarn strength tester, the yarn's characteristic parameters will be obtained, and the physical index of breaking strength, breaking elongation, fracture strength, fracture time and strength tensile curves can be determined, so the quality of yarn will be ascertained (1) . This paper majors in the research of the parameter of the single-yarn strength of yarn. The strength of yarn will increase with increasing within a certain twist; however the strength of yarn will decrease when the twist exceeds the critical value. The relationship between yarn elongation and tension is often nonlinear. When the yarn quality is tested by single-yarn strength tester, the interference noise of test environment in the industrial field is non-Gaussian. So the processing of single-yarn strength testing data is a typical nonlinear and non-Gaussian filtering problem. The traditional digital filtering methods normally only filter out the caused by single reason and can't achieve satisfactory results for complex noise. In order to solve this problem, a large number of nonlinear recursive filtering algorithm such as extended Kalman filtering (EKF), the modified gain Kalman filter (MGEKF), U Kalman filter (UKF) algorithm come forth in recent years, and these filters are based on particular assumption to ensure the optimality. The actual data is usually very complex, and contains many factors such as non-Gaussian, nonlinear, high dimensional and noise; in this case, the Kalman filter generally can't get the analytical solution. This is a common problem and appears in many different kinds of fields (2-11) . Now, a new sequential Monte Carlo particle filter algorithm based on Bayesian principles has been paid more attention. The particle filter technology realize the recursive Bayesian filter with non- parametric Monte Carlo simulation method, which can fit to any state space model and the nonlinear systems which can't be expressed by traditional Kalman filter, and its accuracy approach the accuracy of optimal estimation (12) . This paper places emphasis on introducing the principle and method of the particle filter, and the application of the nonlinear data processing.