[1]王 明,刘建庄.面向煤矿机电设备维护的深度学习故障预测技术研究[J].机械与电子,2025,(08):67-72.
　WANG Ming,LIU Jianzhuang.Research on Deep Learning Fault Prediction Technology for Mechanical and Electrical Equipment Maintenance in Coal Mine[J].Machinery & Electronics,2025,(08):67-72.
点击复制

面向煤矿机电设备维护的深度学习故障预测技术研究()
分享到：

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年08期
页码:
67-72
栏目:
机电一体化
出版日期:
2025-08-25

文章信息/Info

Title:
Research on Deep Learning Fault Prediction Technology for Mechanical and Electrical Equipment Maintenance in Coal Mine
文章编号:
1001-2257 ( 2025 ) 08-0067-06
作者:
王 明 1 ; 刘建庄 2
1. 国能神东煤炭集团,内蒙古 鄂尔多斯 017200 ;?
2. 华北理工大学,河北 唐山 063210
Author(s):
WANG Ming1 ;  LIU Jianzhuang2
( 1.Guoneng Shendong Coal Group , Ordos 017200 , China ;?
2.North China University of Science and Technology , Tangshan 063210 , China )
关键词:
煤矿机电设备; 深度学习; 故障预测
Keywords:
mechanical and electrical equipment in coal mines ;  deep learning ;  failure prediction
分类号:
TP18 ;TD407
文献标志码:
A
摘要:
为解决煤矿机电设备传统维护方法存在效率低、成本高的问题,提出了一种基于深度学习的智能化故障诊断预测方法。该方法集成卷积神经网络( CNN )、长短期记忆网络( LSTM )和门控循环单元( GRU ),并引入卷积核注意力机制动态优化特征提取。利用 CNN 提取振动信号的局部空间特征,LSTM 捕捉长期时序依赖, GRU 增强短时序列处理能力,结合注意力机制自适应融合多尺度特征,并通过 Softmax 完成故障分类。将所提模型应用于实测的煤矿机电设备滚动轴承振动信号中,并和其他模型进行对比。结果表明,不同负载下所提出模型的平均故障诊断准确率为 99.46% ,优于 CNN-LSTM 、 LSTM 和 DNN ,这为煤矿机电设备的智能化运维提供了技术支撑。
Abstract:
In order to solve the problems of low efficiency and high cost of traditional maintenance methods of mechanical and electrical equipment in coal mines , an intelligent fault diagnosis and prediction method based on deep learning was proposed.The method integrates Convolutional Neural Network ( CNN ), long short-term memory network ( LSTM ) and gated recurrent unit ( GRU ), and introduces the convolutional kernel attention mechanism to dynamically optimize feature extraction.CNN is used to extract local spatial features of vibration signals , LSTM captures long-term temporal dependence , GRU enhances short time sequence processing capability , adaptively fuses multi-scale features with attention mechanism , and completes fault classification through Softmax.The proposed model is applied to the measured vibration signal of the rolling bearing of coal mine electromechanical equipment , and compared with other models.The results show that the average fault diagnosis accuracy of the proposed model is 99.46% under different loads , which is better than CNN-LSTM , LSTM and DNN.This advancement provides technical support for intelligent operation and maintenance of coal mine mechanical and electrical equipment.

参考文献/References:

[ 1 ] YANG D L , ZHANG W B , JIANG Y Z.Mechanical fault diagnosis based on deep transfer learning : a review [ J ] .Measurement science and technology , 2023 , 34 ( 11 ): 112001.

[ 2 ] QIN Y , WANG J , MAO Y.Dense framelets with two generators and their application in mechanical fault diagnosis [ J ] .Mechanical systems and signal processing , 2013 , 40 ( 2 ): 483-498.
[ 3 ] GU H.Mechanical fault diagnosis of on-load tap ‐ changer based on improved variational mode decomposition and relative density-based outlier score [ J ] .IET Electric power applications , 2023 , 17 ( 11 ): 1423-1436.
[ 4 ] 朱彦民,李忠虎,王金明,等 . 基于 Transformer 的风电机组故障预测 [ J ] . 电子测量技术, 2024 , 47 ( 13 ): 45-52.
[ 5 ] 龙玉江,姜超颖,钟掖,等 . 极端气象条件下基于深度学习网络特征的变压器故障预测 [ J ] . 现代电子技术,2024 , 47 ( 4 ): 91-96.
[ 6 ] 幸伟鹏 . 基于深度学习的矿山机电设备智能故障预测[ J ] . 中国矿业, 2024 , 33(增刊 1 ): 238-242.
[ 7 ] 张晋恺,马洁 . 基于深度学习的滚动轴承故障预测[ J ] .现代电子技术,2024 , 47 ( 24 ): 120-130.
[ 8 ] 聂磊,张吕凡,徐诗奕,等 . 基于相似度特征融合和 CNN 的滚动轴承剩余寿命预测 [ J ] . 噪声与振动控制,2023 , 43 ( 5 ): 115-121.
[ 9 ] 李恺丽,王剑斌,沈怡俊,等 . 基于 CEEMD-WOA-LSTM 的光伏发电功率预测[ J ] . 热能动力工程, 2025 ,40 ( 2 ): 136-147.
[ 10 ] 蒋琪,张望,屈文忠,等 . 采用门控循环神经网络的核工业管道损伤识别方法[ J ] . 振动与冲击, 2024 , 43( 24 ): 48-58.
[ 11 ] 田晓红,何新卫 . 基于大数据的煤矿安全风险智能评价和预警研究[ J ] . 微型电脑应用, 2022 , 38 ( 12 ): 146-149.
[ 12 ] 赵毅涛,李钊,刘兴龙,等 . 融合卷积神经网络和注意力机制的负荷识别方法[ J ] . 电力工程技术, 2025 , 44( 1 ): 227-235.
[ 13 ] 高宇璠,傅洪贤 . 基于 WOA IC 优化神经网络的隧道爆破振动预测研究 [ J ] . 振动与冲击, 2025 , 44 ( 4 ):229-237.
[ 14 ] FANG D , YUN Z , JING Q.Fault diagnosis of hydro turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network [ J ] .Energy , 2024 , 290 : 130326.
[ 15 ] CHEN H M , MENG W , LI Y J , et al.An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model [ J ] .Measurement science and technology , 2023 , 34 ( 4 ): 045013.
[ 16 ] 龙弟之,李竞元,李天涯,等 . 基于改进 LSTM 的组合式飞轮故障诊断方法[ J ] . 航天控制, 2024 , 42 ( 5 ): 83-88.
[ 17 ] 陈宇,程道来,马向华 . 基于 WDCNN LSTM 混合模型的滚动轴承故障诊断 [ J ] . 机械与电子,2025 , 43( 2 ): 9-15.
[ 18 ] QIN N , WANG T W , HUANG D Q , et al.An open switch fault diagnosis method for single-phase PWM rectifier based on CEEMD-DNN [ J ] .International journal of control automation and systems , 2023 , 21( 10 ): 3430-3442.

相似文献/References:

[1]王骁贤,张保华,陆思良.基于连续小波变换和卷积神经网络的无刷直流电机故障诊断[J].机械与电子,2018,(06):29.
　WANG Xiaoxian,ZHANG Baohua,LU Siliang.Fault Diagnosis of Brushless Direct Current Motor Based on Continuous Wavelet Transform and Convolutional Neural Network[J].Machinery & Electronics,2018,(08):29.
[2]刘志宇,黄亦翔.基于深度学习和迁移学习的液压泵健康评估方法[J].机械与电子,2018,(09):67.
　LIU Zhiyu,HUANG Yixiang.Health Assessment for Hydraulic Pump Based on Deep Learning and Transfer Learning[J].Machinery & Electronics,2018,(08):67.
[3]肖倩宏,康 鹏,杜 江,等.深度学习在电网智能调控系统中应用研究[J].机械与电子,2021,(01):38.
　XIAO Qianhong,KANG Peng,DU Jiang,et al.Research on the Application of Deep Learning Theory in Power Grid Intelligent Dispatching[J].Machinery & Electronics,2021,(08):38.
[4]许 哲,张少帅,郭 璐,等.无人机深度学习去雾算法[J].机械与电子,2021,(04):13.
　XU Zhe,ZHANG Shaoshuai,GUO Lu,et al. Deep Learning Defogging Algorithm for UAV[J].Machinery & Electronics,2021,(08):13.
[5]齐爱玲１,李 琳１,朱亦轩２,等.基于融合特征的双通道CNN滚动轴承故障识别[J].机械与电子,2021,(05):15.
　QI Ailing,LI Lin,ZHU Yixuan,et al.Dual Channel CNN Bearing Fault Identification Based on Fusion Feature[J].Machinery & Electronics,2021,(08):15.
[6]徐先峰,郑少杰,赵 依,等.基于数据分解与重构的光伏发电功率超短期预测[J].机械与电子,2022,(04):20.
　XU Xianfeng,ZHENG Shaojie,ZHAO Yi,et al.Ultra-short-term Prediction of Photovoltaic Power Generation Based on Data Decomposition and Deconstruction[J].Machinery & Electronics,2022,(08):20.
[7]江 励,熊达明,汤健华,等.自然光线环境中的空间物体快速识别和定位算法研究[J].机械与电子,2022,(06):8.
　JIANG Li,XIONG Daming,TANG Jianhua,et al.Recognition and Positioning Algorithm of Space Objects in Natural Light Environment[J].Machinery & Electronics,2022,(08):8.
[8]王西志,管声启,张理博,等.基于视觉引导的工业棒材上料系统研究[J].机械与电子,2023,41(05):19.
　WANG Xizhi,GUAN Shengqi,ZHANG Libo,et al.Research on Industrial Bar Feeding System Based on Visual Guidance[J].Machinery & Electronics,2023,41(08):19.
[9]王 青,吕绪山,党 帅,等.基于深度学习的纱管识别方法研究[J].机械与电子,2023,41(12):20.
　WANG Qing,LYU Xushan,DANG Shuai,et al.Research on Yarn Bobbin Detection Method Based on Deep Learning[J].Machinery & Electronics,2023,41(08):20.
[10]姜越夫,王 青,吕绪山.改进 YOLOv5s 的纱管目标检测方法[J].机械与电子,2024,42(02):29.
　JIANG Yuefu,WANG Qing,LYU Xushan.Improved YOLOv5s Method for Yarn Tube Object Detection[J].Machinery & Electronics,2024,42(08):29.

备注/Memo

备注/Memo:
收稿日期: 2025-03-20
基金项目:河北省自然科学基金项目( 23HBZ085703 )
作者简介:王 明 ( 1984- ),男,河南平舆人,硕士,工程师,研究方向为信息化、智能化等;刘建庄( 1976- ),男,河北唐山人,博士,副教授,研究方向为机械控制等。
更新日期/Last Update: 2025-09-05