舰船电子工程2024，Vol.44Issue(5)：153-158,6.DOI:10.3969/j.issn.1672-9730.2024.05.030
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
摘要
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分类
社会科学引用本文复制引用
刘兴惠,李至立,卢绪迪,孙铭,方玉洁..基于特征选择的半潜式平台故障信号探究[J].舰船电子工程,2024,44(5):153-158,6.基金项目
山东省重大科技创新工程项目(编号:2019JZZY010103) （编号:2019JZZY010103）
烟台市重点研发计划(军民科技融合)(编号:2020JMRH010)资助. （军民科技融合）
