This invention proposes a multi-frequency electromagnetic
feature fusion method for
abrasive particle identification, involving
signal processing, sensor
information fusion, and intelligent monitoring of mechanical conditions. It solves the problems of measurement
distortion caused by hardware parasitic parameter
coupling, the inability to directly solve the highly nonlinear time-
harmonic field model analytically, and the poor convergence of conventional optimization algorithms leading to misjudgment of
abrasive particle parameters in existing
abrasive particle identification methods. This invention constructs a forward analytical model of the time-
harmonic field and a nonlinear objective function, and utilizes the Levenberg-Marquardt (LM) optimization
algorithm to jointly invert and extract the equivalent
diameter,
conductivity, and
relative permeability of unknown metallic abrasive particles in a multi-dimensional parameter space. This invention can accurately decouple the equivalent
diameter,
conductivity, and permeability of abrasive particles, accurately distinguish materials, and the LM
algorithm converges quickly and does not diverge, achieving
millisecond-level inversion, meeting the high precision and real-time requirements of online monitoring of industrial oil.