A Multifrequency Excitation Method for Wear Particle Identification Using an Opposite-Arm Coupled Bridge

Wear particle detection sensors utilizing single-frequency excitation have been widely applied. However, their accuracy in material and size identification remains suboptimal. This study proposes a multifrequency excitation method that enhances the accuracy of both material and size identification by capturing multidimensional particle information. To implement multifrequency excitation while preserving high sensitivity, a novel particle sensor based on an opposite-arm coupled bridge (OACB) is introduced, which is capable of automatically suppressing the residual voltage. A high-bandwidth signal processing circuit is applied to extract the multidimensional particle information. The experimental results validate the effectiveness of the OACB particle sensor in maintaining high sensitivity. For the first time, we conducted particle identification accuracy experiments. A database was established that contains particles from eight different materials to assess the performance of the multifrequency excitation method. The material identification accuracy reached 74.55%, representing an improvement of 17.28% over single-frequency detection. Additionally, the relative error in particle size identification was reduced to 8.34%, which is 4.60% lower than that of single-frequency detection.