基于K-H-Mine算法的废旧机械产品失效信息与再制造加工方案关联规则挖掘

Association rules mining for failure information and remanufacturing machining schemes based on K-H-Mine algorithm of retired products

明确废旧机械产品失效信息与其再制造加工方案间的关联规则,并建立其关联规则库,是实现废旧机械产品再制造加工执行方案高效自主决策的基础和前提。针对大批量废旧机械产品失效信息及其再制造加工数据量大且复杂,导致其关联规则挖掘效率低、有效规则量少的问题,提出一种改进的废旧机械产品失效信息与再制造加工方案关联规则挖掘方法。首先提取再制造案例大数据集核心失效特征,降低数据维度,然后运用改进的K-Means算法对数据集进行聚类,最后运用H-Mine算法并行挖掘其关联规则,并存入规则库。工程实例验证表明,该方法在挖掘效率和挖掘有效关联规则数量上具有明显优势。

Clarifying the association rules between the failure information and remanufacturing machining schemes of retired products,and establishing their association rule base are the basis and premise for realizing efficient self-decision-making of retired products remanufacturing processing execution plan.Aiming at the problem that the data volume of failure information and remanufacturing machining schemes of retired products are large and complex,resulting in low efficiency of mining association rules and few effective rules,an improved association rule mining method for failure information and remanufacturing machining schemes of retired products was proposed.Firstly,the core failure characteristics of the large data set of remanufacturing cases were extracted,and the data dimension was reduced.Then,the improved K-Means algorithm was used to cluster the data set.Finally,the HMine algorithm was used to mine its association rules in parallel and store them in the rule base.The engineering example verification shows that the method has obvious advantages in mining efficiency and the number of effective association rules.