基于集成学习算法的工业产品质量预测  

Quality Prediction in Industrial Products Using Ensemble Methods

作　　者：江琨 丁学明[1] JIANG Kun;DING Xue-ming(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《软件导刊》2019年第1期124-127,共4页Software Guide

基　　金：国家自然科学基金项目(11502145)

摘　　要：现代数字化工业生产中,制造、组装和测试过程会产生大量数据,这些数据中隐藏着决定产品质量的信息和知识。使用传统抽检手段发现质量问题后再加以修改往往为时已晚。数据挖掘中用生产参数预测产品质量,可以预先获取产品质量信息,据此进行调整以提高产品质量。采用CRISP-DM流程,使用集成学习算法(随机森林、XGBoost),利用回归与分类模型进行数据挖掘,经参数调节获得精确的优化模型,在生产中运用该模型有助于提升产品质量。Huge amounts of data in manufacturing,assembly and testing are generated and stored in modern digitalized industrial production.The process data contains inherent knowledge and information,which determines the final quality that needs to be extracted by data mining.In the process of detecting product quality,it is usually late to fix after finding products with poor quality.In data mining,by using process data we can predict product quality in advance and make modifications.In this paper,by using CRISP-DM methodology and ensemble methods(Random Forests and XGBoost),we made a precise quality regression and classification prediction.Accurate optimized models are gained after parameter tuning.These models would be beneficial for improving product quality in practice.

关 键 词：数据挖掘 CRISP-DM 质量预测 集成学习 随机森林 XGboost 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]