中国机械工程2019，Vol.30Issue(2)：244-251,8.DOI:10.3969/j.issn.1004-132X.2019.02.017
基于FP-Growth改进算法的轮胎质量数据分析
Data Analysis of Tyre Quality Based on Improved FP-Growth Algorithm
摘要
Abstract
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.关键词
工业大数据/质量分析/FP-Growth算法/数据挖掘Key words
industrial big data/quality analysis/FP-growth algorithm/data mining分类
信息技术与安全科学引用本文复制引用
李敏波,丁铎,易泳..基于FP-Growth改进算法的轮胎质量数据分析[J].中国机械工程,2019,30(2):244-251,8.基金项目
国家自然科学基金资助项目(61671157) （61671157）
上海科技创新行动计划资助项目(18511107800) （18511107800）
山东省重大科技创新工程资助项目(2018CXGC0604) （2018CXGC0604）
