In order to meet the requirements of condition monitoring of oil system component deterioration at an early stage
an oil-line debris electrostatic sensor has been used to investigate real time and in-line wear monitoring in oil lubricated sliding contacts of bearing steel.The charging mechanism of oil lubricated metallic debris was researched and electrostatic monitoring system was designed.The wear experiments were divided into different loads and speeds
and then the friction coefficient
oil-line debris electrostatic signal
and electrostatic signal root mean square (RMS) were obtained and correlatively analyzed.The results show:(1) the oil-line debris electrostatic method and friction coefficient can consistently detect adhesive wear;(2) the electrostatic monitoring method can detect the abnormal signal before adhesion;(3) in the normal wear stage
the friction coefficient increases with the load reduction
and reduces with the speed increase;(4) in the severe wear stage
the RMS of electrostatic signal pulse peak first increase
Prediction and experiment of vibration wear of aviation spline
Experiment on friction and wear performance of textured port plate pair
Knowledge acquisition for aero-engine wear fault diagnosis based on rule extraction from neural networks
Fusion Diagnosis of Engine Wearing Fault Based on Neural Networks and D-S Evidence Theory
Expert Systems of Engine Wear Fault Diagnosis Based on Knowledge Rule
Related Author
赵广
李盛翔
郭梅
孙红
孙绪聪
韩清凯
LI Yang
DENG Hai-shun
Related Institution
大连理工大学 辽宁重大装备制造协同创新中心
中国航空发动机集团有限公司 沈阳发动机研究所
Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics
College of Mechanical Engineering, Anhui University of Science and Technology
Civil Aviation College, Nanjing University of Aeronautics and Astronautics