Particle imaging, electromagnetic, ferrographic, and spectroscopic methods were used to evaluate hydraulic oil samples collected from machines known to be generating abnormal wear particles. Analytical ferrography confirmed the presence of large reworked ferrous particles and small rubbing wear particles in the oil samples. ICP emission spectroscopy was limited in its ability to detect large ferrous particles. An automated particle imaging system incorporating dual electromagnetic sensors counted the number of ferrous wear particles larger than 25μm in the oil and measured the total ferrous concentration in parts per million. The number of large ferrous wear particles (counts/ml) was found to be independent of the total concentration of ferrous particles (ppm). The ratio of large to small ferrous wear particles and their concentration revealed the severity of wear occurring in the machines. These results demonstrate the diagnostic advantage of combining magnetic and particle imaging sensors in an integrated system.
Detection of abnormal wear particles in hydraulic fluids via electromagnetic sensor and particle imaging technologies