Sensing Model for Detecting Ferromagnetic Debris Based on a High-Gradient Magnetostatic Field

Abstract

Wear debris sensing is an important approach for machine condition monitoring. To date, different types of debris sensors have been developed, among which the magnetostatic inductive debris sensor is preferable for monitoring ferromagnetic debris, but the existing induced voltage model cannot be used to quantify wear debris in lube oil. Accordingly, a modeling method was proposed for the debris sensor based on a high-gradient magnetostatic field. First, a Gaussian curve is used to fit the magnetic field along the axis of the sensor. Then, time-harmonic electromagnetic field analysis and Fourier series decomposition are combined to establish an induced voltage model. Furthermore, the influence of the relative permeability, size, and velocity of wear debris and induction coil parameters on the output voltage is analyzed. The results show a linear relationship between the theoretical calculations and experimental results. Therefore, the proposed model can be used to realize quantitative analysis of wear debris, laying a foundation for optimizing the sensor structure, and extracting features from output voltage signals.