A Microfluidic Oil Particles Monitoring System based on Raspberry Pi

The health status of an aero-engine provides the basic guarantee for the safe flight of an aircraft, and the oil monitoring technology based on oil wear particles analysis is a standard method in the field of aero-engine condition monitoring. This paper aims to design miniaturization, intelligence, and real-time monitoring equipment. Firstly, an experimental monitoring platform is built based on Raspberry Pi. Then images of moving particles flowing through the microfluidic chip are collected by image acquisition software. Finally, the target particles are accurately extracted, and parameters are calculated using significance analysis. The experimental results show that the system is easy to use and provides an efficient and accurate contour identification method, which can be used for intelligent industrial applications in aero-engines and large rotating machines.