Reliability Evaluation and Functional Module Analysis of the Loose Particle Localization Model for Sealed Electronic Equipment

Existing research has constructed the loose particle localization model (LPLM) with excellent classification performance, achieving accurate data classification. However, the loose particle localization results have not been provided, and the construction process of the LPLM has not been comprehensively analyzed. Thus, the practicality of improving classification performance has not been comprehensively understood. Based on this, this article conducts reliability evaluation and functional module analysis of the LPLM. First, the essence of data classification in the LPLM is explored, and a majority voting process is added to achieve the conversion of data classification results to loose particle localization results. Thus, the role of the classification performance of the LPLM in the loose particle localization results is clearly demonstrated. Second, the construction process of the LPLM is comprehensively analyzed, and the impact of the five functional modules on classification performance is quantitatively evaluated and compared, with key functional modules highlighted. Therefore, the practicality of the LPLM is reasonably explained. The experimental results show that, when the classification accuracy achieved by the LPLM based on parameter-optimized XGBoost is stable above 0.53, accurate and reliable loose particle localization results can be obtained. Among them, 'pulse processing' and 'feature construction' are two key functional modules that have a significant impact on the classification performance of the LPLM, with performance degradation amplitudes of 0.1019 and 0.0964, respectively. They need to be taken seriously to maintain stable and excellent classification performance.