Abstract

Anomaly detection of spinning equipment monitors the running state using spectrogram based on quality data of sliver or yarn, thus detecting worn components, equipment faults or incorrect drafting parameters. In order to improve the detection efficiency and accuracy, an assembling anomaly detection method for spectrogram data is proposed in this study that combines traditional spectrum analysis method and machine-learning method. Because of the characteristics of wavelength-spectrum data, this method integrates a data dimensionality reduction method based on extended variable selection, and an isolated forest anomaly detection method, to split out wavelength-spectrum data when a fault occurs. Experimental analysis in study cases from the drawing frame and spinning frame is given to verify the effectiveness of the proposed method, as well as to explain parameter selection for improving detection accuracy.

摘要

纺纱设备异常检测利用基于条子或纱线质量数据的频谱图监测运行状态，从而检测磨损的部件、设备故障或错误的牵伸参数。为了提高检测效率和准确率，本文提出了一种结合传统频谱分析方法和机器学习方法的频谱图数据拼装异常检测方法。该方法针对波谱数据的特点，将基于扩展变量选择的数据降维方法与孤立森林异常检测方法相结合，在故障发生时对波谱数据进行拆分。通过并条机和细纱机的研究案例进行实验分析，验证了所提方法的有效性，