[1]曹家瑜,于洪亮,闫锦,等.基于全频谱技术的油膜诱发转子失稳的故障诊断[J].集美大学学报(自然版),2020,25(3):202-207.
 CAO Jiayu,YU Hongliang,YAN Jin,et al.Diagnostic Analysis of Oil Film Induced Rotor Instability Based on Full Spectrum Plot Technology[J].Journal of Jimei University,2020,25(3):202-207.
点击复制

基于全频谱技术的油膜诱发转子失稳的故障诊断()
分享到:

《集美大学学报(自然版)》[ISSN:1007-7405/CN:35-1186/N]

卷:
第25卷
期数:
2020年第3期
页码:
202-207
栏目:
船舶与机械工程
出版日期:
2020-05-28

文章信息/Info

Title:
Diagnostic Analysis of Oil Film Induced Rotor Instability Based on Full Spectrum Plot Technology
作者:
曹家瑜于洪亮闫锦廖建彬
(集美大学轮机工程学院,福建 厦门 361021 )
Author(s):
CAO JiayuYU HongliangYAN JinLIAO Jianbin
(School of Marine Engineering,Jimei University,Xiamen 361021,China)
关键词:
转子失稳故障诊断油膜全频谱技术
Keywords:
rotor system instabilitydiagnosisoil filmfull spectrum plot technology
摘要:
为了解决传统的频谱技术在转子-轴承系统故障诊断中存在的问题,采用全频谱技术诊断方法,对油膜诱发转子失稳故障进行诊断。该方法可以完整描述转子的进动状态,对整个进动平面进行监测,且能够同时容纳空间上两个垂直的传感器的信号。结果表明,全频谱技术可以对油膜诱发转子失稳的故障进行有效诊断,提高了故障诊断的便捷性与准确度。
Abstract:
In view of the fact that the traditional spectrum plot can only express vibration information in a fixed direction in the fault diagnosis of rotor-bearing system,which has poor reliability and contingency,and causes misjudgment of the results.In this paper,the fault diagnosis of oil film induced rotor instability is carried out by using the full spectrum plot.The full spectrum plot can completely describe the rotor precession state and monitor the whole precession plane,and it can accommodate the signals of two vertical sensors in space.The results show that the full spectrum plot can diagnose the fault characteristics of oil film induced rotor instability,and it can improve the convenience and accuracy of fault diagnosis.

相似文献/References:

[1]王荣杰.基于相似度的电力电子电路故障诊断技术[J].集美大学学报(自然版),2010,15(5):372.
[2]王宁,陈景锋.基于油液监测的柴油机磨损故障诊断系统[J].集美大学学报(自然版),2012,17(3):212.
 WANG NingCHEN Jing-feng.Diesel Engine Wear Fault Diagnosis System Based on the Oil Monitoring Technology[J].Journal of Jimei University,2012,17(3):212.
[3]王永坚,陈景锋,杨小明.基于油液分析的船舶尾轴承状态监测与故障诊断[J].集美大学学报(自然版),2014,19(4):285.
 WANG Yong-jian,CHEN Jing-feng,YANG Xiao-ming.Condition Monitoring and Fault Diagnosis for Ship Stern Bearing Based on Lube Oil Monitoring Analysis[J].Journal of Jimei University,2014,19(3):285.
[4]崔博文.基于小波神经网络的逆变器功率开关故障诊断[J].集美大学学报(自然版),2017,22(1):46.
 CUI Bowen.Open-circuit Faults Diagnosis of Power Device inInverter Based on Wavelet and Neural Network[J].Journal of Jimei University,2017,22(3):46.
[5]罗方芳,陶求华.基于级联极限学习机的基站空调在线监测系统[J].集美大学学报(自然版),2018,23(6):475.
 LUO Fangfang,TAO Qiuhua.Air Conditioning Online Monitoring System for Base Station Based on Cascaded Extreme Learning Machines[J].Journal of Jimei University,2018,23(3):475.
[6]田维,崔博文.基于小波包和支持向量机的逆变器故障诊断[J].集美大学学报(自然版),2019,24(2):125.
 TIAN Wei,CUI Bowen.Faults Diagnosis of Inverter Based on Wavelet Packet Decomposition and SVM[J].Journal of Jimei University,2019,24(3):125.
[7]韩冉,曾广淼,王荣杰.Sepic变换器的开关管故障诊断[J].集美大学学报(自然版),2019,24(4):299.
 HAN Ran,ZENG Guangmiao,WANG Rongjie.Switching Fault Diagnosis of Sepic Converter[J].Journal of Jimei University,2019,24(3):299.
[8]胡欢欢,王永坚,邱晨.HHT和马氏距离融合的船用空压机故障诊断[J].集美大学学报(自然版),2020,25(1):50.
 HU HuanhuanWANG Yongjian,QIU Chen.Research of Fault Diagnosis of Ship’s Air Compressor with HHT Marginal Spectrum and Mahalanobis Distance[J].Journal of Jimei University,2020,25(3):50.

更新日期/Last Update: 2020-07-16