Research on Fault Signal of Semi-submersible Platform Based on Feature Selection
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.
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