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
张万旋 1张箭 1卢哲 1薛薇 1张楠1
作者信息
- 1. 北京航天动力研究所,北京 100076
- 折叠
摘要
为解决液体火箭发动机故障诊断正负样本不平均问题,以及实现发动机稳态工作段自适应故障检测,建立了基于快速增量单分类支持向量机的异常检测模型.采取特征工程方法,对传感器获得的多变量时间序列进行特征提取.通过增量学习方法,对单分类支持向量机模型进行改进,并应用于液体火箭发动机异常检测,使单分类支持向量机检测模型具备对不同台次、不同工况的自适应性,提高了模型的计算速度.对多台次热试车数据的分析结果表明,该模型十分有效,训练速度快,具备实用价值.
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
出版年
2024
国家科技期刊平台
NSTL
万方数据