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基于支持向量机的航空发动机滑油监控分析 被引量：33

Aeroengine Lubrication Monitoring Analysis Via Support Vector Machines
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摘要 提出了一种基于支持向量机的航空发动机滑油金属含量预测方法。详细分析了支持向量机用于时间序列预测的理论基础,并给出了运用支持向量回归进行多步预测的一般公式,提出了用最终预报误差(FPE)准则优化选取嵌入维数。与传统的AR预测模型相比,支持向量机由于采用了新型的结构风险最小化准则表现出优秀的推广能力。经过数值仿真得出自回归(AR)模型仅适合于短期预测;支持向量机预测推广能力强、具有较强的鲁棒性和容错性,对较长区间预测仍具有较好的效果。最后,将其应用于某型发动机滑油的铁金属含量预测,取得了较好的效果。 A novel aeroengine lubrication monitoring method based on support vector machines is presented in this paper.Basic theory analysis of support vector regression in time series forecasting is introduced in detail and a multi-step forecasting formula is presented,Final Prediction Error (FPE) principle is suggested to select the embedding dimension.Compared with general autoregressive forecasting method it adopts new type of structural risk minimization principle and thus it owns excellent generalization ability.During numerical simulations,we infer that Auto-Regressive (AR) forecasting method is suitable to short intervals while Support Vector Machines (SVM) still possesses good robustness and fault-tolerant virtue in metaphase intervals forecasting.Finally,some typs of aeroengine's lubrication metal content have been monitored for feasibility validation and test results are satisfactory.
作者 尉询楷 李应红 王硕 路建明 汪诚
机构地区 空军工程大学工程学院 山东青岛即墨鹤山工业园
出处 《航空动力学报》 EI CAS CSCD 北大核心 2004年第3期392-397,共6页 Journal of Aerospace Power
关键词 支持向量机 航空发动机 滑油监控 自回归模型 时间序列预测 金属含量 aerospace propulsion system lubrication monitoring auto-regressive model support vector regression time series forecasting
分类号 V233.4 [航空宇航科学与技术—航空宇航推进理论与工程]
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参考文献14

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航空动力学报
2004年 第3期

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