摘要
为提高煤矿生产的安全性与效率,提出煤矿采煤机牵引部轴承故障自动诊断方法研究。首先系统剖析牵引部轴承的常见故障类型、根源及其对机械性能的影响,深入探索了振动信号采集与预处理方法,以精准提取故障特征。然后提出了一种改进的支持向量机算法模型,通过优化核心函数与参数配置,实现对故障诊断准确性与时效性的强化,为煤矿安全生产提供有力技术支持。最后应用实验验证所提方法的先进性,结果表明,该方法对故障的平均识别准确率达96.4%,对正常状态的检测正确率高达99.2%,故障识别平均耗时25.2 ms,说明该方法应用效果较好。
To improve the safety and efficiency of coal mining production,a research on automatic diagnosis method for bearing faults in the traction part of coal mining machines is proposed.Firstly,the common fault types,root causes,and their impact on mechanical performance of traction bearings were systematically analyzed,and vibration signal acquisition and preprocessing methods were deeply explored to accurately extract fault features.Then,an improved support vector machine algorithm model was proposed to enhance the accuracy and timeliness of fault diagnosis by optimizing the core function and parameter configuration,providing strong technical support for coal mine safety production.Finally,the progressiveness of the proposed method is verified by experiments.The results show that the average recognition accuracy of the method for faults is 96.4%,the detection accuracy for normal conditions is 99.2%,and the average time for fault recognition is 25.2 ms.This shows that the application effect of the method is good.
作者
董高杰
DONG Gaojie(Fengshuigou Coal Mine,Inner Mongolia Pingzhuang Coal Industry(Group)Co.,Ltd.,Chifeng,Inner Mongolia 024081,China)
出处
《自动化应用》
2025年第7期82-84,共3页
Automation Application
关键词
采煤机
牵引部
轴承故障
故障诊断
自动化
改进的支持向量机算法模型
coal mining machine
traction department
bearing malfunction
fault diagnosis
automation
improved support vector machine algorithm model
作者简介
董高杰,男,1979年生,研究方向为采矿工程。
