摘要
针对半潜式平台系统故障警报信号频发、系统运行稳定性差等问题,基于半潜式平台工作信号点位数据集研究影响半潜式平台稳定运行的重要因素。基于这些影响因素采用机器学习、深度学习算法构建基于深度神经网络(Deep Neural Network,DNN)的平台系统稳定性预测模型,该模型的曲线下面积(Area Under Curve,AUC)得分较逻辑回归、K近邻查询、支持向量机、朴素贝叶斯等传统机器学习模型的AUC得分提高1.0%~16.0%、准确率提高3.0%~25.6%,表明DNN模型具有较好的拟合能力和泛化能力,可以用于工业实践。
Aiming at the problems of frequent fault alarm signals and poor system stability of semi-submersible platform,the important factors affecting the stable operation of semi-submersible platform is studied based on the data set of working signal points of semi-submersible platform.Based on these factors,a platform system stability prediction model based on Deep Neural Network(DNN)is constructed by machine learning and deep learning algorithm.The Area Under Curve(AUC)score of the model is improved by 1.0%-16.0%and the accuracy is improved by 3.0%-25.6%compared with those of the traditional machine learning models such as Logistics Regression(LR),K-Nearest Neighbor query(KNN),Support Vector Machine(SVM)and Naive Bayesian(NB),which indicates that the DNN model is of good fitting ability and generalization ability,and can be used in industrial practice.
作者
李至立
却立勇
刘兴惠
LI Zhili;QUE Liyong;LIU Xinghui(Shandong Vheng Data Technology Co.,Ltd.,Yantai 264003,Shandong,China)
出处
《中国海洋平台》
2022年第2期33-38,共6页
China offshore Platform
基金
山东省重大科技创新工程项目“深水半潜式平台运行及海况数据的认知方法和分析模型”(编号:2019JZZY010103)。
关键词
半潜式平台
系统稳定性
预测模型
DNN
机器学习
semi-submersible platform
system stability
prediction model
DNN
machine learning
