关键词: 盾构机, 装配质量, 风险分析, 关联规则挖掘, FP-Growth算法

Abstract: Based on the research of traditional assembly quality issues, a shield machine assembly quality optimization model which driven by association rule mining was constructed, and intelligent decision-making optimization methods for assembly quality defects were investigated. A transactional database was built for assembly defects based on the assembly defect data of a domestic shield machine manufacturing enterprise from 2019 to 2024 which the multi-dimensional information such as fault components,classification,and problem level was structured and coded,considering the process characteristics such as production attributes and occurrence stages. An assembly quality risk mining model was established based on the FP-Growth algorithm. Combined with indicators such as support,confidence,and improvement, high reliability and high certainty association rules extraction of quality problems were realized. Through visualization methods such as three-dimensional rule distribution surface maps and causal network diagrams,the combination path of high-risk components-causes-defects was intuitively revealed. The experimental results of association rule mining show that the proposed method can effectively identify the key risk factors affecting the quality of shield assembly,and provide strong data support for subsequent quality control optimization and process management.

Key words: shield machine, assembly quality, risk analysis, association rule mining, FP-Growth algorithm