摘要
通过对矿井提升机故障机理的研究,提出矿山设备信息的优势特征频率提取的方法,构造基于FTA-SVM优化算法的智能故障诊断模型,将其应用于矿井提升机工程实例中,并与人工诊断结果进行对比,同诊断结果基本一致,验证了该方法的可行性,从而解决了智能诊断方法中先验知识库匮乏的问题,也充分体现了基于FTA-SVM智能故障诊断方法的训练速度快、诊断精度高和自适应能力强等特点。
Dominant characteristic frequency extraction method of mining equipment is proposed through the research of the mine hoist fault mechanism, and constructs the intelligent fault diagnosis model based on FTA-SVMoptimization algorithm,and it＇s applied in mine hoist project instances, andeompared with the artificial diagnosis,the diagnostic results are same basically ,the validity of the above method is proved, and it has solved the lack of priori knowledge of intelligent diagnosis method, it also illustrates the features of the intelligent diagnosis method on FTA-SVM： fast training rate, high diagnostic accuracy, strong self-adaptive ability and so on.
出处
《煤矿机械》
2017年第4期180-183,共4页
Coal Mine Machinery
基金
国家自然科学基金青年科学基金项目(41002075)
