基于特征优化和支持向量机的航空发动机气路故障诊断
Aeroengine Gas-Path Fault Diagnosis Based on Feature Optimization and Support Vector Machine
吴超 1陈磊 2刘渊 1周绮凤 2王奕首2
作者信息
- 1. 中国航发湖南动力机械研究所,湖南株洲 412002
- 2. 厦门大学 航空航天学院,福建厦门 361005
- 折叠
摘要
针对现有数据驱动的航空发动机故障诊断算法易受飞行监控数据中冗余特征及噪声的干扰,不能及时修正监测数据中不平衡样本分布对模型泛化性能影响等问题,通过在支持向量机模型中引入特征增维和采用提取算法,提出基于特征优化和支持向量机的航空发动机气路故障诊断方法,并建立相应模型.将涡桨发动机及CFM56-7B发动机航后数据输入模型,分析与预测实际故障发生时刻,并将预测结果与真实结果进行比较,同时将其结果与采用随机森林等4种故障诊断方法所得结果进行对比验证.结果表明:特征优化算法的应用能显著缩短各类故障诊断方法运行时间20%以上;基于特征优化和支持向量机的故障诊断方法使预测准确率达99.8%;针对非平衡实测数据,特征优化算法和回归预测思想的引入能显著提高算法在不平衡数据集上的性能,与非回归算法相比故障检测率提高到91.67%.
Abstract
Aiming at the problems that existing data-driven aeroengine fault diagnosis algorithms are susceptible to the disruptive effects of redundant features and noise in flight monitoring data,and unable to timely address the impact of imbalanced sample distribution in monitoring data on the model's generalization performance,by introducing feature augmentation and using extraction algorithms in sup-port vector machine models,an aeroengine gas-path fault diagnosis method was proposed based on feature optimization and the support vector machine,and the corresponding model was established.A simulation dataset from a turboprop engine and a flight dataset from a CFM56-7B engine were input into the model to analyze and predict the fault occurrence time.The predicted results were compared with the actual results,and the latter compared with those obtained by four fault diagnosis methods such as random forest.The results show that the application of the feature optimization algorithm can significantly shorten the computational time of various fault diagnosing methods by more than 20%;the fault diagnosis method based on feature optimization and support vector machine achieves a prediction accuracy of 99.8%;for unbalanced measured data,the introduction of feature optimization algorithm and regression prediction can significantly improve the performance of the algorithm on imbalanced datasets,and the fault detection rate is improved to 91.67%compared with non-regression algorithms.
关键词
故障诊断/特征优化/支持向量机/主成分分析/深度自编码器/航空发动机Key words
fault diagnosis/feature optimization/support vector machine/principal component analysis/deep autoencoder/aeroengine引用本文复制引用
基金项目
国家级基础加强项目(2019-JCJQ-ZD-339-00)
出版年
2024
NETL
NSTL
万方数据