基于改进ReliefF-PNN的航空发动机起动系统异常状态识别

Abnormal state identification of aircraft engine starting system based on the improved ReliefF-PNN

摘要

在航空发动机起动系统异常状态的识别研究中，起动系统的参数具有数据间相关性强、数据维度高、数据冗余信息多等特点。为降低数据维度，提高异常状态识别的准确率，将改进ReliefF算法与概率神经网络（probabilistic neural network，PNN）结合，提出改进ReliefF-PNN的航空发动机起动系统异常状态识别方法，更加有效地降低了参数的维度，并提升了异常状态识别模型的性能。利用该模型针对起动系统进行识别验证和分析。结果表明，利用改进后的ReliefF-PNN算法得到的参数子集进行异常状态识别的准确率优于改进前的结果，模型性能得到了进一步改善。

Abstract

In the study of abnormal state identification of aircraft engine starting systems， the parameters of the starting system have characteristics such as strong correlation between data， high data dimensions and a lot of redundant information in data. In order to reduce the data dimension and improve the accuracy of abnormal state identification， an improved ReliefF algorithm was combined with probabilistic neural network（PNN） and improved ReliefF-PNN was proposed for abnormal state identification of aircraft engine starting system， which effectively reduced the dimension of parameters and improved the performance of the abnormal state identification model. The obtained model was used for identification verification and analysis of the starting system.The results show that the accuracy of using the improved ReliefF-PNN algorithm to identify abnormal states is better than before， further improving the performance of the model.

关键词

发动机起动系统 / 参数选择 / ReliefF算法 / 概率神经网络 / 异常状态识别

Key words

engine starting system / parameter selection / ReliefF algorithm / PNN / abnormal state identification

基金资助

国家自然科学基金(62003223)