针对电纺丝成型纳米级纤维工艺中因供料速度与电压值不匹配而出现的不稳定现象-流涎，提出了基于目标多特征识别的纳米纤维制造在线检测系统来提高纳米纤维成型质量的稳定性并实现纤维直径的可控性。首先，使用工业CCD对喷头出口处的泰勒锥进行连续图像采集，并对其进行锐化、滤波、阈值分割等预处理; 然后，用目标多特征识别算法对泰勒锥的周长、面积和锥高等特征进行识别，实现对泰勒锥的形态和大小实时判别; 最后，以判别结果作为控制系统的反馈信号，实时在线调节供料模块或电源模块驱动供料速度与电压达到匹配。实验结果表明，当电压为8 kV，系统能在0.43 s后达到稳定，从而维持泰勒锥形状稳定，无流涎现象，得到直径均匀的纳米纤维。提出的方法有效地解决了电纺纳米纤维直径不均匀和不可控制的难题。

To solve the problem of salivate, an unsteady phenomenon caused by the mismatch of feed velocity and high-voltage in the electrospinning to fabricate nanoscale fibers, this paper proposes an on-line monitor system based on the multi-featured pattern recognition for realizing both the stability of nanofiber manufacturing and the controllability of fiber diameter. Firstly,an industry CCD is used to collect the image of the Taylor cone continously, and then these images are preprocessed by sharpening, filtering and threshold segmentation. The perimeter, area and height of the Taylor cone are recognized by the multi-featured pattern recognition algorithm to judge the shape and size of the Taylor cone. Finally, the result of pattern recognition is viewed as the feedback signal to adjust the feed module and high-voltage module. Experimental results indicate that when the voltage is 8 kV, the system can be stabilized in 0.43 s,and it maintains a stable shape of Taylor cone without the salivate phenomenon.Moreover,obtained nanoscale fiber shows a uniform diameter. In conclusion, this method has reliably and effectively solved the instability and uncontrollability of nanoscale fiber manufarturing.