摘要
针对现有煤矿机电设备故障预测技术的不足,以已有的煤矿机电设备点检数据为基础,提出基于最小二乘支持向量机(LS-SVM)的煤矿机电设备健康预测技术。以大同煤矿集团公司马脊梁矿DTL1400型胶带输送机高速轴非驱动端温度为点检参数,对LS-SVM的设备健康趋势预测方法进行了验证。实验结果表明,LS-SVM能够很好地对煤矿机电设备故障进行预测。
For the lack of the fault prediction technology of the existing coal mine electromechanical equipment, on the basis of the point check data of the existing coal mine electromechanical equipment, the paper puts forward health prediction technology of coal mine electromechanical equipment based on LS-SVM. The non-driving end temperature of high-speed shaft of DTL1400 belt conveyor in Majiliang Mine of Datong Coal Mine Group as the tally parameter, equipment health trend prediction method of LS-SVM is validated. The experimental results show that LS-SVM can predict the fault of coal mine electromechanical equipment well.
出处
《同煤科技》
2014年第1期21-23,28,共4页
Datong Coal Science & Technology
基金
2013年度大同煤炭职业技术学院校级课题(DTMTXYKT2013002)
关键词
机电设备
健康管理
最小二乘支持向量机
electromechanical equipment
health management
least squares support vector machine
