Abstract：The loose particles generated during the production process of sealed electronic equipment pose a severe threat to their operational reliability. The particle impact noise detection method can detect the presence of loose particles; it cannot identify their attributes, such as location and material. Recognizing the attributes of loose particles has become a hot topic. In practical applications, a single detection task typically involves multiple detection events, and existing methods lack the mechanism to convert the classification results from these events into a single recognition result for the task. The impact of loose-particle attribute recognition models at the detection-event level on recognition accuracy at the detection-task level remains unclear. In addition, the contribution of each functional module in loose particle attribute recognition models needs to be evaluated to explain the synergistic effect between performance reliability and recognition stability. This paper proposes a framework for reliability evaluation and interpretability analysis of loose particle attribute recognition models. Firstly, by introducing majority voting, the classification results for all detection events in a single detection task are aggregated, and the category with the highest frequency is determined as the attribute recognition result. Thus, a conversion mechanism from event classification to task recognition is established, and the issue of converting attribute recognition results in practical applications is solved. Secondly, to evaluate the robustness of the loose particle attribute recognition models, a novel definition of recognition reliability has been proposed. That is, under the same operating conditions, when the model's performance shows reasonable degradation, as long as its global classification performance and the local classification performance for each category remain above 0.5, the accuracy of attribute recognition results can still be guaranteed through majority voting. Thus, the impact of regularity on the relationship between performance reliability and recognition accuracy is clarified. Then, cumulative progressive, individual progressive, and sequential failure analysis methods are used to evaluate the five functional modules during model construction. It is shown that pulse processing and feature construction are the two key functional modules with the greatest impact on model performance. Finally, numerous experiments in real-world sealed electronic equipment detection scenarios have shown that the loose particle attribute recognition models can consistently achieve global and local classification performance above 0.5 under varying operating conditions. Therefore, accurate and reliable location and material recognition results can be continuously produced through majority voting, demonstrating the proposed framework's practicality and robustness. This paper provides a reference for the reliability evaluation and robustness application of machine learning models.
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2026, Vol. 41 
