ZHANG Y Y, LI W H, YAN L D ,et al.Fault diagnosis method for mine roller mill based on multi-source information fusion[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):40-47.

doi:10.16186/j.cnki.1673-9787.2025100055

Received:2025/10/25

Revised:2025/12/20

Published:2026-07-24

Fault diagnosis method for mine roller mill based on multi-source information fusion

Zhang Yuyan1, Li Wenhao1, Yan Lingdi2, Li Hao1, He Wenbin1, Ming Wuyi1, Wen Xiaoyu1, Wang Haoqi1

1.Henan Key Laboratory of Mechanical Equipment Intelligent Manufacturing， Zhengzhou University of Light Industry， Zhengzhou  450000， Henan， China；2.Mining Machinery Research Institute， CITIC Heavy Industry， Luoyang  471000， Henan， China

Abstract: Objectives Traditional fault diagnosis methods often rely on a single data source， such as vibration 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 extract 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 converges 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 accuracy of 98.9%. The multi source information fusion method significantly outperforms the single source methods， 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.

Key words:multi-source information fusion;fault diagnosis;boxplot;t-SNE dimensionality reduction;neural network