Online Yarn Breakage Detection: A Reflection-Based Anomaly Detection Method

Abstract

This article discusses an optical reflection-based method for equipment anomaly detection that enhances weak signals and has high sensitivity to abnormalities. Automatic warp knitting machine yarn breakage detection, which has become an acknowledged difficulty in the textile field, is achieved. To the best of our knowledge, this is the first time that visual inspection has been applied to yarn breakage detection in weaving. Furthermore, based on the periodicity of the yarn distribution and the periodic motion law of the machine, the combined wavelet and Seasonal and Trend decomposition using locally weighted regression (LOESS) (STL) decomposition method is proposed for yarn breakage detection. Finally, the efficiency and accuracy of the proposed method are verified experimentally. Our research is a successful application of reflection characteristics to the anomaly detection of non-Lambertian objects, which has implications for the high-precision anomaly detection of precision equipment.