基于特征选择的半潜式平台故障信号探究
Research on Fault Signal of Semi-submersible Platform Based on Feature Selection
刘兴惠 1李至立 1卢绪迪 2孙铭 1方玉洁1
作者信息
- 1. 山东纬横数据科技有限公司 烟台 264000
- 2. 中集海洋工程研究院有限公司 烟台 264000
- 折叠
摘要
"蓝鲸2号"第七代深水半潜式钻井平台,其工作环境恶劣且远离港岸,故保障平台的平稳运行和安全是重中之重.该平台电力系统警报信号特征种类众多、特征重要程度模糊,仅利用单分类器方法无法准确划分故障警报信号,因此,引入集成学习算法并结合特征选择技术,提出基于支持向量机递归特征消除(SVM-RFE)的Bagging-AdaBoost分类模型(SRBA),用于解决多特征分类问题.结果显示提出的SRBA集成学习算法综合分类正确率达96%,在分类精度上优于Bag-ging、AdaBoost、Bagging-AdaBoost分类器对比模型,该方法具有较高的稳定性和分类准确度,是一种更为有效的分类手段.
Abstract
The seventh-generation deep-water semi-submersible drilling platform of"Blue Whale 2"has a bad working envi-ronment and is far away from the port shore,so ensuring the smooth operation and safety of the platform is the top priority.The plat-form has many kinds of features and fuzzy importance of power system alarm signals.Only using single classifier method can not ac-curately classify fault alarm signals.Therefore,an integrated learning algorithm and feature selection technology are introduced to propose a Bagging-AdaBoost classification model based on Support Vector Machine Recursive Feature Elimination(SVM-RFE)to solve the classification problem with multiple features(SRBA).The results show that the comprehensive classification accuracy of the proposed SRBA ensemble learning algorithm reaches 96%,which outperforms the Bagging,AdaBoost,Bagging-AdaBoost clas-sifier comparison models in classification accuracy.It shows that this method has high stability and classification accuracy,and is a more effective classification method.
关键词
深水半潜式平台/故障警报信号/特征选择/Bagging/AdaBoostKey words
deep-water semi-submersible platform/failure alarm signal/feature selection/Bagging/AdaBoost引用本文复制引用
基金项目
山东省重大科技创新工程项目(2019JZZY010103)
烟台市重点研发计划(军民科技融合)(2020JMRH010)
出版年
2024
国家科技期刊平台
NSTL
万方数据