Machine learning based anomaly detection and diagnosis method of spinning equipment driven by spectrogram data

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.