电子测量与仪器学报

王梦雅,刘丽冰,熊桂龙,赵丹琳,王宇.面向袋式除尘器的大数据挖掘 XGBoost 优化算法研究[J].电子测量与仪器学报,2020,34(7):159-167

面向袋式除尘器的大数据挖掘 XGBoost 优化算法研究

Research on big data mining XGBoost optimization algorithm for bag dust collector

DOI：

中文关键词: 袋式除尘器大数据挖掘 XGBoost 模型蚁群算法优化破袋监测

英文关键词:bag filter big data mining XGBoost model ant colony algorithm optimization broken bag monitoring

基金项目:国家自然科学基金(51666011)、江西省自然科学基金(20171ACB21008)资助项目

作者	单位
王梦雅	1. 河北工业大学机械工程学院
刘丽冰	1. 河北工业大学机械工程学院
熊桂龙	2. 南昌大学资源环境与化工学院，3. 鄱阳湖环境与资源利用教育部重点实验室
赵丹琳	1. 河北工业大学机械工程学院
王宇	1. 河北工业大学机械工程学院

Author	Institution
Wang Mengya	1. School of Mechanical Engineering, Hebei University of Technology
Liu Libing	1. School of Mechanical Engineering, Hebei University of Technology
Xiong Guilong	2. School of Resources Environmental and Chemical Engineering, Nanchang University,3. Key Laboratory of Poyang Lake Environment and Resource Utilization, Ministry of Education
Zhao Danlin	1. School of Mechanical Engineering, Hebei University of Technology
Wang Yu	1. School of Mechanical Engineering, Hebei University of Technology

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中文摘要:

袋式除尘器在产品生命周期不同阶段,包括设计、仿真、制造、测试实验以及运维等阶段都会产生大量数据,挖掘产品大数据与其运行特性之间复杂、非线性、耦合的内在关联,为解决袋式除尘器行业设计创新、运维优化等关键共性技术提供新思路。针对袋式除尘器大数据特点,提出了一种用于袋式除尘器滤袋破损在线监测的大数据挖掘 XGBoost 模型,研究了基于蚁群算法的 XGBoost 模型参数优化方法。研究结果表明,与随机森林、BP 网络挖掘模型相比,XGBoost 优化模型方法准确度高,识别速度快,可解释性强。

英文摘要:

In different stages of product life cycle, including design, simulation, manufacture, test and operation and maintenance, bag filter generates a large amount of data. It excavates the complex, non-linear and coupling internal relationship between big data of product and its operation characteristics, and provides a new way to solve the common problems of design innovation and operation and maintenance optimization in bag filter industry. Aiming at the characteristics of large data of bag filter, a large data mining XGBoost model for on-line monitoring of bag breakage of bag filter is proposed, and the parameter optimization method of XGBoost model based on ant colony algorithm is studied. Compared with Stochastic Forest and BP network mining models, the results show that the XGBoost optimization model method has high accuracy and strong explanability.

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