| 王奕首1，余映红1，卿新林1，殷锴2，赵奇2.基于KPCA 和DBN 的航空发动机排气温度基线模型[J].航空发动机,2020,46(1):54-60 |
| 基于KPCA 和DBN 的航空发动机排气温度基线模型 |
| Exhaust Gas Temperature Baseline Model of Aeroengine Based on Kernel Principal Component Analysisand Deep Belief Network |
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| DOI： |
| 中文关键词: 健康管理 排气温度 核主成分分析 深度置信网络 航空发动机 |
| 英文关键词:health management exhaust gas temperature KPCA DBN aeroengine |
| 基金项目:中央高校基本科研业务项目（20720180120）资助 |
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| 中文摘要: |
| 为了给航空发动机整体性能的实时监控与健康管理提供技术手段，提出1 种基于核主成分分析和深度置信网络相结合
的航空发动机排气温度基线模型构建方法。以配装CFM56-7B 发动机的飞机在运行过程中各系统产生的快速存取数据作为原始的
数据样本，利用核主成分分析进行降维处理，选用高斯函数作为核函数，将降维后的数据作为深度置信网络的输入，建立航空发动
机EGT 基线模型，通过大量QAR 数据验证了模型的有效性和正确性。与传统神经网络建模方法相比，所提出的建模方法不但降低了
网络结构的复杂度，同时也提高了模型的精度。 |
| 英文摘要: |
| In order to provide technology means for real-time monitoring and health management of the overall performance of
aeroengine，a method based on Kernel Principal Component Analysis（KPCA）and Deep Belief Network（DBN）was proposed to construct the
baseline model of aeroengine exhaust gas temperature. The Quick Access Recorder （QAR）data generated by the aircraft equipped with
CFM56-7B engine in the course of operation was taken as the original data sample. The KPCA was used to reduce the dimension，the
Gaussian function was selected as the kernel function，and the reduced data was used as the input of the DBN. The EGT baseline model of
aeroengine is established，and the validity and correctness of the model are verified by a large number of QAR data. Compared with the
traditional neural network modeling method，the proposed modeling method not only reduces the complexity of the network structure，but
also improves the accuracy of the model. |
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