基于支持向量机与粗糙集的隔爆电动机故障诊断

基金项目:

国家自然科学基金项目（51277149）

陕西省教育厅科研计划项目（14JK1472）

详细信息

Fault diagnosis of explosion proof motor based on SVM and RS

1.
School of Electrical and Control Engineering, Xi''an University of Science and Technology, Xi''an 710054, China
2.
Branch of Mining Machinery Manufacturing Maintenance Co., Ltd., Shenhua Ningxia Coal

摘要: 针对煤矿井下隔爆电动机故障数据获取难且故障数据杂乱、非线性等问题，提出了一种基于支持向量机与粗糙集的隔爆电动机故障诊断方法。该方法采用小波包对隔爆电动机定子瞬时功率进行频谱分析，并提取故障特征量；利用粗糙集的约简特性消除故障特征量冗余数据，将约简后的故障特征量作为支持向量机的输入样本，实现隔爆电动机转子故障诊断和分类。仿真结果表明，该方法故障诊断结果准确率达到92.857 1%。
- 隔爆电动机 /
- 故障诊断 /
- 支持向量机 /
- 粗糙集 /
- 瞬时功率 /
- 小波包
Abstract: In view of problems that fault data acquisition of explosion proof motor in underground coal mine was difficult and fault data was clutter and nonlinear, a fault diagnosis method of explosion proof motor based on SVM and RS was proposed. Wavelet packet is used to analyze instantaneous power of stator of explosion proof motor, and extract fault feature. Redundant data of fault feature is eliminated using reduction feature of rough set, and the reduced feature is used as input sample of support vector machine, to realize rotor fault diagnosis and classification of explosion proof motor. The simulation results show that fault diagnosis accuracy of the proposed method reaches 92.857 1%.
- explosion proof motor /
- fault diagnosis /
- SVM /
- RS /
- instantaneous power /
- wavelet packet

1.	杨敬娜，郝克明，朱霄珣，董勇敢. 激励下齿轮-转子系统故障模糊C聚类诊断. 机械设计与制造. 2023(04): 296-299+304 . 百度学术
2.	张德宇，罗玉梅. 粗糙集下网络大数据混合属性特征检测仿真. 计算机仿真. 2021(01): 460-463+485 . 百度学术
3.	潘红光，宋浩骞，苏涛，马彪. 基于SVM的煤炭低位发热量软测量. 西安科技大学学报. 2021(06): 1130-1137 . 百度学术
4.	周明春. 矿山机械设备故障的检测方法研究. 世界有色金属. 2019(08): 60+62 . 百度学术
5.	孙海霞，木合塔尔·克力木，王晨，李卉. RS-CS-SVM在电液伺服系统故障诊断中的应用. 组合机床与自动化加工技术. 2018(06): 47-50+55 . 百度学术
6.	刘洋，丁云飞. 风力发电机典型智能故障诊断方法综述. 上海电机学院学报. 2017(06): 353-360+372 . 百度学术