面向飞机装配质量数据深度分析方法研究

针对飞机装配质量数据体系化不高,数据挖掘利用率低下等问题,探索出一种面向飞机装配质量数据深度分析方法。通过建立由质量数据关系模型和质量数据仓库模型共同组成质量数据模型体系,设计出一种基于数据模型体系的飞机装配质量数据挖掘循环机制。采用聚类算法选择质量问题关键特征点,同时运用Apriori算法寻找各关键因素的关联关系,最终借助Weka软件实现装配质量数据深度分析。以某型飞机前机身部件装配质量数据验证了该方法的可行性和有效性。

Research on depth analysis method for aircraft assembly quality data

The purpose of the study is to explore a new method to deeply analyze aircraft assembly quality data, due to the low level of quality data system in aircraft assembly and low utilization of data mining. Aircraft assembly data mining cycle mechanism was designed based on data model system, which was composed by quality relation data model and the quality data warehouse model. Clustering algorithm was used to find key points of quality problem, and at the same time, Apriori algorithm was used to find the relationship between the key points, finally the depth analysis of assembly quality data was realized by Weka software. The assembly quality data of a certain type of ex-aircraft fuselage improved the feasibility and effectiveness of the method.