基于PSO-SVM-RF的离心泵转子故障诊断研究  

Research on the Rotor Fault Diagnosis of the Centrifugal Pump Based on PSO-SVM-RF

作　　者：范传翰 宋礼威 刘厚林[1] 董亮[1] 肖幸鑫 陈泽宇 FAN Chuan-han;SONG Li-wei;LIU Hou-lin;DONG Liang;XIAO Xing-xin;CHENG Ze-yu(Fluid Machinery Engineering Technology Research Center of Jiangsu University,Zhenjiang 212000,Jiangsu Province,China;State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment of CGN Engineering Co.,Ltd,Shenzhen 518172,Guangdong Province,China)

机构地区：[1]江苏大学流体机械工程技术研究中心,江苏镇江212000 [2]中广核工程有限公司核电安全监控技术与装备国家重点实验室,广东深圳518172

出　　处：《中国农村水利水电》2023年第2期171-176,共6页China Rural Water and Hydropower

基　　金：国家自然科学基金项目(51879122,51779108,51779106);镇江市重点研发计划项目(GY2017001,GY2018025);西华大学流体及动力机械教育部重点实验室开放课题项目(szjj2017-094,szjj2016-068);江苏高校优势学科建设工程项目(PAPD);江苏省“六大人才高峰”高层次人才项目(GBZB-017)。

摘　　要：转子是离心泵的重要组件之一,对转子运行状态的检测和诊断的研究具有重要意义。转子不平衡、不对中故障引发的故障特征较为相似,为了有效识别离心泵这两种转子故障。通过在离心泵进口法兰位置布置振动加速度传感器进行信号采集,提取原信号时频域特征,并利用随机森林算法筛选出重要性较高6个特征并将随机森林得到的分类结果作为PSO-SVM的输入,进而来区分正常、转子不对中、转子不平衡故障,同时还比较了该方法与传统PSO-SVM的故障识别率。结果表明,该模型PSO优化迭代次数更少、具有更高的识别率,对故障的识别率达到99.36%。Rotor is one of the important components of centrifugal pump, which is of great significance to the detection and diagnosis of rotor running state. The characteristics of faults caused by unbalanced and misaligned rotor faults are similar. In order to effectively identify the two rotor faults of centrifugal pump rotation, the influence of location selection of different measuring points on distinguishing faults is effectively understood. Sensors are arranged on the inlet flange of centrifugal pump for signal acquisition, and the time-frequency domain characteristics of the original signal are extracted. The random forest algorithm is used to screen out 6 features with high importance, and the classification results obtained by the random forest are used as the input of PSO-SVM, so as to distinguish the faults of normal, misaligned rotor and unbalanced rotor. At the same time, the fault recognition rate of this method is compared with that of traditional PSO-SVM. The results show that the PSO model has fewer iterations and higher recognition rate, and the recognition rate of faults reaches 99.36%.

关 键 词：离心泵 故障诊断 支持向量机 随机森林 转子不平衡 转子不对中 

分 类 号：TH311[机械工程—机械制造及自动化]