Research on Wear Particle Online Monitoring Using Machine Vision for Rotating Machinery

A high-precision wear particle monitoring instrument based on numerical simulation, machine vision, multitarget tracking algorithm, and microfluidic technology is designed and developed in this article. The instrument not only performs the traditional particle number and size detection but also proposes a particle density detection model based on motion velocity. First, an online visual microfluidic chip for oil particle motion was specially designed. Then, a mathematical model relationship between particle velocity and particle diameter, particle density, oil viscosity, and oil density was established by force analysis and solving the equation, which was used to explain the working principle of the instrument. Meanwhile, a multitarget tracking algorithm incorporating the characteristics of wear particles based on YOLOv5-Deepsort was custom-developed to detect the number, size, and motion characteristics of particles under the optical field of view of the image sensor. Second, the oil flow field distribution and the four factors affecting the particle motion characteristics in the self-developed chamber, particle size, particle material, particle radial position, and oil velocity, were analyzed by using the finite-element analysis in COMSOL. Finally, pin-on-disk wear experimental validation results demonstrated this approach has excellent suitability to monitor the number, size, and density of wear particles with diameters above
$20 \mu \text{m}$
and densities above 2000 kg/
$\text{m}^{3}$
simultaneously. This portable oil particle monitoring instrument provides technical support for condition monitoring, fault diagnosis, and intelligent maintenance of rotating mechanical equipment.