Deep Information Detection Method for Loose Particles Inside Sealed Electronic Equipment From Signal and Pulse Perspectives

Loose particles inside sealed electronic equipment pose a serious threat to their reliable operation. Particle impact noise detection (PIND) method can identify their presence (shallow information), while obtaining their deep information (i.e., material and location) provides key basis for accurate management and cleaning of loose particles. Current research works focus on the pulses in loose particle signals, constructing feature vectors or spectrograms, and training classification models for loose particle material identification and localization. However, they ignore the complete motion state contained in the entire signal, which is precisely the key to localization. In this study, the authors first proposed and demonstrated the complementarity and applicability of signal perspective and pulse perspective in detecting deep information of loose particles. Specifically, the entire signal provides feedback on the motion process of “contact, bounce suspension, and recontact” of loose particles and the corresponding phase, which is suitable for loose particle localization. The pulse directly carries the contact energy of loose particles, which is suitable for loose particle material identification. On this basis, the authors proposed a deep information detection method for loose particles from signal and pulse perspectives. It systematically compared the classification effect of classification models in material identification and localization tasks, which were trained on datasets and image sets constructed from two perspectives. Finally, experiments validated and determined the optimal solution, i.e., the combination of pulse perspective and spectrogram technology is the optimal way to achieve loose particle material identification, while the combination of signal perspective and feature engineering is the optimal way to achieve loose particle localization. Experimental results in real application scenarios fully confirmed the feasibility, practicality, and superiority of the proposed method, ensuring the reliability of the deep information detection results of loose particles.