Identification of Size and Distribution Features of Spherical Magnetic Wear Particles in Engine Lubricant

To address the issue of signal aliasing when multiple particles pass through a metallic particle sensor, which can lead to misidentification of particle count, we employ numerical simulation methods for an in-depth investigation. We developed a mathematical model of a three-coil inductive metal particle sensor to explore the signal variations induced by the passage of a single particle. We utilized micro-element simulation analysis to dissect the signal generated by a single particle, elucidating the underlying change process. Focusing on dual ferromagnetic particles as the subject of study, we conducted simulations and demodulation of the induced voltage under various combinations of sizes and spacings to investigate the influence patterns of dual adjacent ferromagnetic particles on the sensor's induced signal. Further research into the peak signals of different diameter particles at a constant spacing revealed that, for a given spacing, the ratio of peak signals between particles of varying diameters and those of a single particle remains relatively consistent. We then extended the scope of the study to simulate multiple adjacent particles, decomposed multi-particle signals based on the characteristics of dual-particle signals, and proposed a method for identifying the number of closely spaced particles. Additionally, using a single-particle signal model, our analysis of multi-particle signals demonstrated that the diameter, quantity, and spacing of particles can be identified to some extent by examining the distances and magnitudes of peaks and troughs in the multi-particle signal. Our findings provide theoretical support and technical references for the accurate identification of multiple particles by inductive sensors.