Material Recognition Technology of Internal Loose Particles in Sealed Electronic Components Based on Random Forest

Abstract

Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material identification technology achieves an accuracy rate of 85.7% in distinguishing between metal and nonmetal materials, and an accuracy rate of 73.8% in identifying specific materials. This result surpasses the accuracy rates achieved by all currently known identification techniques. At the same time, this technology represents the latest expansion in the field of loose particles detection and holds significant practical value for improving system robustness. The proposed technique theoretically can be widely applied to other fault diagnosis fields with similar signal generation mechanisms.