河南理工大学学报（自然科学版）2026，Vol.45Issue(5)：40-47,8.DOI:10.16186/j.cnki.1673-9787.2025100055
基于多源信息融合的矿山辊磨机故障诊断方法
Fault diagnosis method for mine roller mill based on multi-source information fusion
摘要
Abstract
Objectives Traditional fault diagnosis methods often rely on a single data source,such as vibra-tion data or temperature data,resulting in low accuracy when applied to mine roller mills with multiple fault excitation sources.To address this issue,a fault diagnosis method based on multi source information fusion is proposed.Methods First,the boxplot method is used for statistical analysis to label all samples as normal or abnormal.Then,multi source data are preprocessed using t-SNE dimensionality reduction to ex-tract the most critical features.Subsequently,the reduced dimension data are fed into a neural network for training.Meanwhile,three types of single source data samples(current,temperature,and pressure)are also extracted.Finally,the multi source fused data and the single source data are input separately into the neural network to obtain the diagnostic results.Results Taking the multi sensor data from the high pressure roller mill of CITIC Heavy Industries Co.,Ltd.as an example,comparative experiments on fault diagnosis accuracy are conducted using both multi source fused data and single source data.The experimental results show that:for the current source data,the training converges after 218 iterations,with a training time of 466 s and a test accuracy of 90.4%;for the pressure source data,training converges after 672 iterations,with a training time of 1 426 s and a test accuracy of 94.1%;for the temperature source data,training con-verges after 658 iterations,with a training time of 1 247 s and a test accuracy of 86.1%.In contrast,the multi source fused data reaches the target threshold after only 36 iterations,with a training time of 77 s and a test ac-curacy of 98.9%.The multi source information fusion method significantly outperforms the single source meth-ods,markedly reducing the diagnosis error.Conclusions The fault diagnosis method based on multi source information fusion can substantially improve fault diagnosis accuracy and is a highly effective approach.关键词
多源信息融合/故障诊断/箱形图/t-SNE降维/神经网络Key words
multi-source information fusion/fault diagnosis/boxplot/t-SNE dimensionality reduction/neu-ral network分类
机械制造引用本文复制引用
张玉彦,李文浩,闫玲娣,李浩,何文斌,明五一,文笑雨,王昊琪..基于多源信息融合的矿山辊磨机故障诊断方法[J].河南理工大学学报（自然科学版）,2026,45(5):40-47,8.基金项目
国家自然科学基金资助项目(52105536) （52105536）
广东省基础与应用基础研究基金资助项目(2022A1515140066) （2022A1515140066）
河南省重点研发专项项目(221111240200) （221111240200）
河南省重点研发与推广专项项目(科技攻关)(232102221009) （科技攻关）
