Multi-Source Data-Driven Framework for Work State Classification in Fabric Pilling and Linting Performance Assessment

Abstract

Fabric pilling performance is one of the key indicators for evaluating textile quality, but there is limited research on the effectiveness of pilling detection traceability and non-intrusive monitoring of detection equipment operating status. In this paper, a multi-source data-driven method for classifying the working status of fabric pilling performance detection is proposed. This study constructs a real-time non-intrusive monitoring system for fabric pilling detection in a laboratory environment, collecting multiple types of data such as electrical parameters of pilling detection equipment, personnel behavior, and equipment noise. GoogLeNet convolutional neural network is used to recognize high-dimensional audio data and achieve feature dimension reduction. By constructing a multi-classification algorithm based on Decision Tree-Support Vector Machine (DT-SVM) for the pilling detection process, a minimum accuracy of 95.62% is achieved in practical operation. This system not only perceives the relevant influencing factors of detection activities without interfering with normal detection activities but also effectively distinguishes various detection working states, providing new ideas for the effectiveness traceability of pilling detection activities.