国防科技大学学报2024，Vol.46Issue(2)：115-122,8.DOI:10.11887/j.cn.202402012
火箭发动机故障检测的快速增量单分类支持向量机算法
Fast incremental one-class support vector machine algorithm for rocket engine fault detection
摘要
Abstract
In order to solve the problem of imbalance between positive and negative samples in liquid rocket engine fault diagnosis,and to enable adaptive fault detection during engine steady working state,a anomaly detection model based on fast incremental one-class support vector machine was established.Feature engineering method was adopted to extract features from sensor-obtained multivariate time series.Through incremental leaning,the one-class support vector machine model was improved and applied to liquid rocket engine anomaly detection.The one-class support vector machine detection model was endowed with adaptability for various engine individuals and multiple working conditions,while increasing computing speed.The analysis results of multiple hot test data show that the model is effective,fast-training and practically valuable.关键词
单分类支持向量机/特征提取/自适应检测/增量学习/异常检测Key words
one-class support vector machine/feature extraction/adaptive detection/incremental learning/anomaly detection引用本文复制引用
张万旋,张箭,卢哲,薛薇,张楠..火箭发动机故障检测的快速增量单分类支持向量机算法[J].国防科技大学学报,2024,46(2):115-122,8.基金项目
国家自然科学基金资助项目(52232014) （52232014）
