| 李业波，蒋平国，田迪，俞明帅，文彬鹤.航空发动机传感器解析余度模型的建立方法[J].航空发动机,2018,44(4):67-71 |
| 航空发动机传感器解析余度模型的建立方法 |
| Modeling Method of Analytical Redundancy Model of Sensors for Aero-engine |
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| DOI： |
| 中文关键词: 传感器 解析余度模型 极端学习机 K- 均值聚类 微分进化算法 航空发动机 |
| 英文关键词:sensors analytical redundancy model extreme learning machine K-means clustering differential evolution aeroengine |
| 基金项目:航空动力基础研究项目资助 |
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| 中文摘要: |
| 为了使用解析余度模型对传感器故障进行诊断，提出了1 种基于K- 均值聚类与改进微分进化算法优化的极端学习机
（IDE-ELM）的发动机传感器解析余度模型建立方法。为避免求解ELM 算法时H 矩阵奇异，采用K- 均值聚类对试验数据进行聚类处
理，然后从每类数据中选取1 组数据组成训练样本用于训练；利用IDE 算法优化ELM 的输入层权值和偏置，提高ELM 的泛化能力。
利用飞行试验数据进行了仿真验证。结果表明：基于K- 均值聚类和IDE-ELM 设计的传感器解析余度模型具有较高的精度，可用于
FADEC 系统双通道传感器的故障诊断 |
| 英文摘要: |
| In order to diagnose sensor fault using analytical redundancy model, a modeling method for analytical redundancy model of
sensors was proposed based on K-means clustering and extreme learning machine (ELM) optimized by improved differential evolution (IDE)
algorithm. To avoid the H matrix singularity during solving the ELM algorithm, K-means clustering was used to cluster the test data, and
then a set of data was selected from each kind of data to form training samples. The IDE algorithm was used to optimize the input layer
weight and bias of ELM, which could improve the generalization ability of ELM algorithm. The simulation experiments using flight test data
was carried out. The results show that the established analytical redundancy model of sensor based on K-means and IDE-ELM achieves
high accuracy and can be used to dual-channel sensors diagnosis |
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