现代纺织技术2025，Vol.33Issue(11)：43-53,11.DOI:10.12477/j.att.202501018
基于ARIMA-贝叶斯网络与混合修复方法的纺纱机异常数据处理
Abnormal data processing of spinning machines based on ARIMA-Bayesian network and hybrid repair method
摘要
Abstract
In response to the issue of abnormal data arising from communication failures,data acquisition and transmission anomalies in the data collection process of spinning machines,this paper proposes a dual-dimensional abnormal data identification and hybrid missing data repair method to enhance data completeness and accuracy.Therefore,how to effectively identify abnormal data and accurately repair missing data is a key issue to ensure the normal operation of textile equipment and improve the precision of data analysis. The dual-dimensional abnormal data identification method proposed in this paper combines analytical models from both the time dimension and the parameter dimension,fully leveraging the complementary advantages of the autoregressive integrated moving average(ARIMA)model and the Bayesian network model.In the time dimension,the ARIMA model is used to analyze the single-parameter time series data to identify anomalies in individual parameters over time.In the parameter dimension,this paper uses the Bayesian network model to construct causal relationships among multiple parameters,thereby detecting anomalies related to the interactions between different parameters.Since there is usually a strong correlation between multiple parameters of the spinning machine,it may not be possible to fully identify anomalies by analyzing a parameter alone.This paper calculates the Spearman rank correlation coefficient between parameters to construct a correlation matrix and generates a causal network graph based on this matrix,identifying anomalies at a specific time point that are inconsistent with changes in other parameters.After the abnormal data identification is completed,in order to ensure data completeness and accuracy,this paper proposes a hybrid repair method to fill in missing data.According to the characteristics of the spinning machine data,the K-nearest neighbors(KNN)algorithm is used to repair stable fluctuating data.The KNN algorithm fills in missing values by comparing the similarity between the missing point and its neighboring points and selecting the closest neighboring value.For continuously increasing data,a piecewise linear regression combined with a sliding window prediction method is used to repair. The experimental results show that the dual-dimensional abnormal data identification method proposed in this paper achieves an identification rate of 97.58％,and the average fitting coefficient of the hybrid repair method reaches 0.9614,which verifies the effectiveness and reliability of the proposed method.The method presented in this paper can identify abnormal data more comprehensively and repair different types of missing data accurately,providing significant technical support for the intelligent production of spinning machines and the optimization of spinning processes.关键词
纺纱机/异常数据识别/数据修复/ARIMA模型/贝叶斯网络模型/时间序列数据Key words
spinning machine/abnormal data identification/data repair/ARIMA model/Bayesian network model/time series data分类
轻工纺织引用本文复制引用
候松松,戴宁,胡旭东,沈春娅,丁春高..基于ARIMA-贝叶斯网络与混合修复方法的纺纱机异常数据处理[J].现代纺织技术,2025,33(11):43-53,11.基金项目
浙江省科技计划项目(2022C01065) （2022C01065）
