摘要
针对轮胎制造过程质量异常的问题分析,介绍了轮胎质量数据获取、有效整合与数据分析流程,基于Hive数据仓库构建了生产数据与产品检测数据相关联的结构化数据集。针对现有频繁模式增长(FP-Growth)算法存在FP树建树性能较低与大数据处理效率低的问题,提出了一种改进的FPGrowth算法,在原有的频繁项头表基础上新增一个tail属性,加速FP树构建。实验结果表明,改进后的FP-Growth并行算法能够有效提高轮胎质量异常数据的关联分析效率,能够找出影响轮胎质量的生产制造重要因素,并且适用于大数据量的数据挖掘。
According to the problem analyses of abnormal quality in tyre manufacturing processes,tyre quality data acquisition,effective integration and data analysis processes were discussed.The structured data sets associated with production data and product inspection data were constructed based on Hive data warehouse.For the existing frequent pattern-growth(FP-Growth)algorithm,the performance of FP-tree was low,an improved FP-growth algorithm was proposed.A new tail attribute was added to the existing header table of frequent item and accelerate the construction of FP-tree.The experiments show that the improved FP-growth algorithm may effectively improve the correlation analysis efficiency of tyre quality abnormal data.The improved FP-growth algorithm is able to identify the factors that affect the quality of tire productions,and it is also suitable for large data mining.
作者
李敏波
丁铎
易泳
LI Minbo;DING Duo;YI Yong(Software School,Fudan University,Shanghai,200433;Shanghai Key Laboratory of Data Science,Fudan University,Shanghai,200433)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2019年第2期244-251,共8页
China Mechanical Engineering
基金
国家自然科学基金资助项目(61671157)
上海科技创新行动计划资助项目(18511107800)
山东省重大科技创新工程资助项目(2018CXGC0604)
