现代制造工程 ›› 2017, Vol. 447 ›› Issue (12): 25-29.doi: 10.16731/j.cnki.1671-3133.2017.12.005

• 实验研究 • 上一篇    下一篇

电动机运行状态在线智能监测系统研究

谢锋云, 江炜文, 陈红年, 李钊, 唐宏兵, 张慧慧   

  1. 华东交通大学机电与车辆工程学院,南昌 330013
  • 收稿日期:2016-11-03 出版日期:2017-12-20 发布日期:1900-01-01
  • 作者简介:谢锋云,博士,副教授,研究方向为先进检测技术、不确定性分析。 E-mail:xiefyun@163.com
  • 基金资助:
    国家自然科学基金项目(51565015)

Research on on-line intelligent monitoring system of motor running state

Xie Fengyun, Jiang Weiwen, Chen Hongnian, Li Zhao, Tang Hongbing, Zhang Huihui   

  1. School of Mechatronics & Vehicle Engineering,East China Jiaotong University,Nanchang 330013,China
  • Received:2016-11-03 Online:2017-12-20 Published:1900-01-01

摘要: 电动机是机械加工中不可或缺的必备工具,电动机在运转中常产生各种故障,为保证电动机运行安全,对电动机运行状态进行在线监测尤为重要。以三相异步电动机为研究对象,采用传感器获取电动机运行中的重要参数(振动、噪声、转速及温度等),由时/频域分析及能量分析等方法提取电动机运行特征量,构成特征向量,采用BP神经网络训练的方法建立状态识别模型,通过BP神经网络模式识别方法,判断电动机运行的状态,在此基础上,利用LabVIEW软件构建可视化监测系统,将电动机运行参数及状态实时显示在可视化界面中,完成在线智能监测。

关键词: 电动机, 状态, 神经网络, 监测

Abstract: Motor is an indispensable tool in machining.The various faults often take place in the motor operation.In order to ensure the safe running of the motor,it is particularly important for on-line monitoring of the motor running state.Three-phase asynchronous motor was regarded as the research object.The important parameters of the motor running (vibration,sound,speed and temperature) were obtained by the sensor acquisition.The motor running features were extracted by the time-frequency analysis and energy analysis,and those features constituted feature vectors.The state recognition model was built according to BP neural network training method.The motor running state was evaluated according to BP neural network pattern recognition method.The visualization monitoring system was constructed by using LabVIEW software.The motor running parameters and the states were real-time displayed in the visual interface,and the on-line intelligent monitoring was completed.

Key words: motor, state, neural network, monitoring

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