摘要
传统电机轴承润滑脂含量判定方法通过停转进行润滑脂含量测量,这种方法使用起来极其不便利,而且很多轴承的润滑脂都是密封的。针对其停转、拆封的不便,提出了一种基于BP神经网络的电机轴承润滑脂含量判定方法。利用采集到的电机轴承的振动数据,对其进行统计特征提取,通过BP神经网络来构建润滑脂含量与统计特征之间的函数关系。经试验验证,该方法可以有效地检测出电机轴承润滑脂含量,对电机轴承的维修保护有着重要意义。
The traditional determination method of grease content of motor bearing needs to measure the grease content of the motor bearing when it stops running.The grease of many bearings is sealed.Aiming at the inconvenient of the method,a determination method of grease content of motor bearing based on BP neural network was presented.The collected vibration data of motor bearing was used to extract its statistical features,and the functional relationship between grease content and statistical features was constructed through BP neural network.The experimental results show that the method proposed can effectively detect the grease content of motor bearing,which is of great significance to the maintenance and protection of motor bearing.
作者
王辉
林加剑
蔡孟翔
WANG Hui;LIN Jiajian;CAI Mengxiang(Bengbu Design&Research Institute for Glass Industry Co.,Ltd.,Bengbu 233010,China;School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China)
出处
《现代制造工程》
CSCD
北大核心
2021年第2期91-94,共4页
Modern Manufacturing Engineering
基金
安徽省青年人才基金项目(2013SQRL009ZD)
中国博士后科学基金项目(20100481453)
青年骨干教师资助项目(33010226)
青年人才基金项目(Z010118121)。
