基于自组织特征映射网络的采煤机故障诊断  

Fault Diagnosis of Shearer based on Self-organizing Feature Mapping Network

作　　者：常莹莹 林园园 徐彤 Chang Yingying;Lin Yuanyuan;Xu Tong(College of Science and Arts,Jiangsu Normal University,Jiangsu Xuzhou 221132)

机构地区：[1]江苏师范大学科文学院,江苏徐州221132

出　　处：《山东煤炭科技》2022年第12期187-189,192,共4页Shandong Coal Science and Technology

摘　　要：针对采煤机日常运行中各类故障难以诊断的问题,以MG300/700-WD型交流电牵引采煤机为研究对象,通过提取采煤机的故障特征信息,并结合相关样本数据,提出了一种基于自组织特征映射(SOM)网络的采煤机故障诊断模型。经Matlab仿真测试,采用SOM网络进行采煤机故障诊断具有一定的可行性和实用性。Aiming at the problem that it is difficult to diagnose all kinds of faults in the daily operation of shearer, taking the MG300/700-WD type AC electric haulage shearer as the research object, by extracting the fault feature information of shearer and combining with relevant sample data, a shearer fault diagnosis model based on self-organizing feature map(SOM) network is proposed. Through the Matlab simulation test, it is feasible and practical to use SOM network to diagnose the fault of shearer.

关 键 词：自组织特征映射 采煤机 故障诊断 故障特征 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]