Large-diameter circular knitting machine rotation period prediction method and system using adaptive filter

Abstract

The invention relates to a large-diameter circular knitting machine rotation period prediction method and system using an adaptive filter, and aims at the phenomenon that the rotation period of a large-diameter circular knitting machine is unstable in the actual production in the textile industry. The filter combines the advantages of an RLS period prediction algorithm and a single step period prediction algorithm, the rotation period of the large-diameter circular knitting machine in the actual production can be effectively and accurately predicted. The method and system of the invention areclosely combined with a defect detection visual system, the prediction period can be quickly and accurately obtained through real-time analysis and adaptive filtering process of a serial port signal,the prediction period can be used to calculate the rotational speed of the large-diameter circular knitting machine, an image that needs to be captured by the defect detection visual system is positioned, defects in the entire batch fabric are also positioned, and a basis is laid for the realization of a highly effective gray cloth detection system. The method and system of the invention can quickly and accurately predict the period of the large-diameter circular knitting machine, reduce the difficulty of accurate capture of the defect detection visual system, and improve the positioning accuracy of the defects.

Description

technical field [0001] The invention relates to the field of production measurement and control of large circular knitting machines in the textile industry, in particular to a method and system for predicting the rotation period of large circular knitting machines using an adaptive filter, which is applied to a defect detection system with complex production environments and unstable rotation states of large circular knitting machines. Background technique [0002] In the traditional production of large circular knitting machines, manual inspection is often used to monitor the defective cloth in production, but the traditional method is prone to subjective interference of inspection workers and is inefficient. With the popularization of industrial video surveillance and the development of video image processing technology, more and more researches on intelligent defect detection based on machine vision appear in people's field of vision. Previous researches on intelligent de...

AI Technical Summary

Problems solved by technology

The occurrence frequency of the defect information of the gray cloth is low, and a large amount of image information will be wasted by using the frame-by-frame analysis method. Due to the influence of the actual production environment of the large circular knitting machine and the limitation of its own long-term production life conditions, it will become difficult to use the timing capture method to set an appropriate time. extremely difficult

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