Loose particle is an important factor leading to the failure of sealed electronic components, and conducting loose particle detection is crucial. The presence of component signals seriously affects loose particle detection, thus accurately identifying detected signals becomes the key. Existing studies ignored various types of detected signals, and only considered training single-structure classifiers from the vector perspective, resulting in limited classification effect and application ability. Based on this, an integrated model construction method from perspectives of multiple information carriers was proposed. Four types of detected signals in real scenarios were considered for the first time, and techniques such as feature engineering, image processing, and audio processing were used to convert detected signals into vectors, time-domain images, spectrograms, and audios, which belong to the information carrier of vectors, images, and audios. Then, the dataset, time-domain image set, spectrogram image set, and audio set were created. Classifiers or neural networks suitable for vectors and images were trained, parameter optimization was performed to obtain the optimal vector-classifier, time-neural network, and spectrogram-neural network. One neural network suitable for audios was proposed and optimized, and audio-neural network was obtained. On this basis, decision-level fusion technology was referenced, and applicable fuzzy rules and weight calculation formulas were newly proposed, from which an integrated model was constructed. Experiments show that the integrated model achieves a significant classification accuracy of 94.94%. Applications in real scenarios show that the integrated model achieves the highest and most stable classification accuracy of 92.31%. Ablation experiments and robustness validations fully demonstrate the feasibility, practicality, and superiority of the proposed method. This study is an important supplement to existing research on loose particle detection, providing reference for multisource information fusion in similar fields.

Component signal seriously affects the loose particle detection results. The existing research focused on pure loose particle and component signals, training suitable classifiers to classify the data of two labels from two signals. However, in real application scenarios, pure signals rarely appear, and the data classification results are not the required signal recognition or loose particle detection results. The feasibility and practicality of the existing research are limited. In this article, the authors proposed a loose particle detection method based on the recognition of overlapping signals. By obtaining the optimal recognition model and standard confidence probability, the pure and overlapping signals can be accurately recognized, and the loose particle detection can be realized in a comprehensive manner. Multiple detection results in real application scenarios indicated that the obtained overlapping signal recognition and loose particle detection results were stable and reliable. Compared with the existing research, the loose particle detection sensitivity has been significantly improved.