基于CNN-BES-ELM的航空发动机气路故障诊断研究
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1.南昌航空大学飞行器工程学院 南昌 330063; 2.南昌航空大学通航学院 南昌 330063

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TN707

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江西省双千计划(jxsq2018106057)项目资助


Research on aero-engine gas path fault diagnosis based on CNN-BES-ELM
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1.College of Aircraft Engineering,Nanchang Hang Kong University,Nanchang 330063,China; 2.College of Aviation,Nanchang Hang Kong University,Nanchang 330063,China

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    摘要:

    针对航空发动机运行过程中出现的气路故障问题,提出了一种基于卷积神经网络(CNN)结合秃鹰搜索算法(BES)优化极限学习机(ELM)的航空发动机气路故障诊断模型。通过CNN对航空发动机气路数据进行学习并提取出隐藏在数据中的故障特征,引入BES算法对ELM的权重和偏置进行寻优,使用优化后的ELM对CNN所提取的抽象特征进行分类,从而达到故障诊断的目的。实验结果表明,基于CNN-BES-ELM的模型其平均准确率达到了97.80%,较CNN-ELM、CNN和ELM等方法分别提高了2.7%、5.4%和7.35%,较常用的深度学习模型如深度置信网络(DBN)和堆叠自编码器(SAE),其准确率分别提高了5.4%和3.4%;并且在随机噪声、高斯噪声和椒盐噪声等噪声环境下仍保有90%以上的准确率,整体表现出良好的诊断性能、泛化能力和抗噪能力,为其在航空发动机气路故障诊断中的实际应用提供了理论依据。

    Abstract:

    Aiming at the airway fault problems occurring during the operation of aero-engine, an aero-engine airway fault diagnosis model based on Convolutional Neural Network (CNN) combined with Bald Eagle Search Algorithm (BES) Optimized Extreme Learning Machine (ELM) is proposed. The aero-engine airway data are learned by CNN and the fault features hidden in the data are extracted, the BES algorithm is introduced to optimize the weights and biases of the ELM, and the optimized ELM is used to classify the abstract features extracted by the CNN, so as to achieve the purpose of fault diagnosis. The experimental results show that the CNN-BES-ELM-based model achieves an average accuracy of 97.80%, which is 2.7%, 5.4%and 7.35% higher than that of CNN-ELM, CNN and ELM, respectively, and compared with commonly used deep learning models such as Deep Belief Network (DBN) and Stacked Auto Encoder (SAE), the accuracy is improved by 5.4% and 3.4%; and still retains more than 90% accuracy in noise environments such as random noise, Gaussian noise and pretzel noise, which overall shows good diagnostic performance, generalization ability and noise immunity, and provides a theoretical basis for its practical application in aero-engine airway fault diagnosis.

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戴郎杰,蔡开龙,王阿久,黄菲.基于CNN-BES-ELM的航空发动机气路故障诊断研究[J].电子测量技术,2024,47(13):27-34

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  • 在线发布日期: 2024-11-07
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