Design of a Nested Inductive Wear Particle Sensor

Lubricating oil contains abundant information reflecting equipment status. This study proposes a nested inductive sensor to address the need for wear particle detection in lubricants. A mathematical model of the sensor was established based on the electromagnetic induction principle. From the perspective of coil mutual inductance, the sensitivity advantage of the nested configuration was analyzed. Transient electromagnetic simulations validated the theoretical findings. Experimental verification using a prototype system successfully identified 71-µm ferromagnetic and 373-µm non-ferromagnetic particles within a 6-mm flow channel, with feature extraction converting signals into identifiable pulse waveforms. To enhance signal clarity, an OVMD-ICR algorithm (Optimizing Variational Mode Decomposition Combined with Independent Component Reconstruction) was integrated, outperforming conventional denoising methods. This advancement enables real-time monitoring of lubrication systems, providing a foundation for predictive maintenance strategies. A sensor system was constructed, the detection performance for different wear particles was tested, and an OVMD-ICR noise reduction algorithm was proposed. The sensor detected 71-µm ferromagnetic and 373-µm non-ferromagnetic particles, within a 6-mm channel, as experimentally verified. This provides support for developing integrated online wear particle detection systems. It is also significant for wear particle detection in lubrication systems.