Energy release usually accompanies the single-fiber tensile fracture, and can be monitored using acoustic emission technology. Generated during the process of molecular structure fracture of various fibers, the acoustic emission signals can be extracted to identify different fracture types of fiber, which is especially important to the yarn formation process. In this study, a low-noise fiber-stretching device was employed to process the weak-intensity signal generated during fiber tensile fracture; in addition, the Hilbert–Huang transform (HHT), principal component analysis (PCA) and least squares support vector machine (LSSVM) algorithms were combined to identify the collected acoustic emission signals of polyester and cotton fibers. At the same time, it was verified that compared with the single-fiber breaking acoustic emission signal obtained by the electronic single-fiber strength tester, the signal acquisition device based on pneumatic components proposed in this paper can significantly improve the signal-to-noise ratio of the signal. According to the algorithm recognition results, the recognition rate of the two fibers increased from 74% to 95%.The experimental results indicate successful measurements of different fractures of two types of fiber.

中文翻译：

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基于声发射法的纤维拉伸断裂机器学习方法识别纤维

能量释放通常伴随着单纤维拉伸断裂，并且可以使用声发射技术进行监测。各种纤维分子结构断裂过程中产生的声发射信号，可以提取出不同的纤维断裂类型，这对纱线形成过程尤为重要。本研究采用低噪声纤维拉伸装置处理纤维拉伸断裂过程中产生的微弱信号；此外，结合希尔伯特-黄变换（HHT）、主成分分析（PCA）和最小二乘支持向量机（LSSVM）算法对采集到的涤纶和棉纤维声发射信号进行识别。同时，经验证，与电子单纤强度测试仪获取的单纤断裂声发射信号相比，本文提出的基于气动元件的信号采集装置能够显着提高信号的信噪比。根据算法识别结果，两种纤维的识别率从74%提高到95%。实验结果表明，成功测量了两种纤维的不同断口。