Research on an Intelligent Piezoelectric Needle Selector System with Closed-Loop Fault Detection Capability

Abstract

The piezoelectric needle selector is a crucial component of computerized dobby weft knitting machines. With the continuous development of weft knitting machine technology, enhancing the accuracy of piezoelectric needle selector control is essential. Accurate determination of whether the blades are in the correct position would significantly improve the precision of piezoelectric needle selector control. In this study, piezoelectric ceramic sensors were used to collect impact vibration signals when the blades struck the damper baffle. Key hardware circuits were designed for this purpose. A self-learning algorithm was employed to capture the highest point and the time it takes to reach the highest point in the impact vibration signal. A fault detection algorithm was used to implement closed-loop fault detection for piezoelectric needle selectors. Experimental results and practical applications have demonstrated that this research effectively addresses the accurate determination of whether the piezoelectric needle selector blades are in the correct position. It has reduced the defect rate of fabric production in weft knitting, thereby improving the overall efficiency and profitability of businesses.

Keywords

• piezoelectric needle selector
• impact vibration signals
• self-learning program algorithm
• fault detection program algorithm
• closed-loop fault detection