| 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 |
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| 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 |
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| 摘要点击次数: 928 |
| 全文下载次数: 1970 |
| 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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