航空发动机气路部件故障融合诊断方法研究
收稿日期: 2013-07-09
修回日期: 2014-02-18
网络出版日期: 2014-06-20
基金资助
国家自然科学基金（51006052，61104067）；航空科学基金（20110652003）；中央高校基本科研业务费专项基金（NZ2012104）；江苏省2012年度普通高校研究生科研创新计划（CXZZ12_0169）
Research on Gas Fault Fusion Diagnosis of Aero-engine Component
Received date: 2013-07-09
Revised date: 2014-02-18
Online published: 2014-06-20
Supported by
National Natural Science Foundation of China(51006052,61104067); Aeronautical Science Foundation of China(20110652003); The Fundamental Research Funds for Central Universities(NZ2012104); Graduate Student Research and Innovation Program of Jiangsu Province(CXZZ12_0169)
针对发动机气路部件故障，提出了一种基于模型和基于数据驱动的融合诊断方法。采用极端学习机（ELM）实现基于数据驱动的故障诊断。针对ELM随机选择输入层权值和隐含层偏置带来的缺点，采用改进微分进化（IDE）算法以训练样本的均方根误差（RMSE）和输出层权值的范数为评价标准对其进行优化，减少了ELM的隐含层节点数，提高了网络的泛化能力。同时，由于传感器数目的不足，采用基于奇异值分解（SVD）的Kalman（SVD-Kalman）滤波器实现基于模型的部件故障诊断。为了提高航空发动机部件故障诊断的精度，利用改进的迭代约简最小二乘支持向量回归机（IRR-LSSVR）算法对两种算法的估计结果在特征层进行定量融合。仿真结果表明，在发动机稳态状态下，与单独使用基于模型和数据驱动的诊断方法相比，采用特征层融合有效地提高了部件故障诊断的精度和准确率。
李业波 , 李秋红 , 黄向华 , 赵永平 . 航空发动机气路部件故障融合诊断方法研究[J]. 航空学报, 2014 , 35(6) : 1612 -1622 . DOI: 10.7527/S1000-6893.2013.0543
A model-based and data-driven-based aero-engine component fault fusion diagnosis method is presented in this paper. The extreme learning machine (ELM) is used to the data-driven-based component fault diagnosis. However, the ELM input weights and hidden layer biases are generated randomly, which usually leads to more nodes in the hidden layer and poor generalization ability. To overcome these drawbacks of ELM, an improved differential evolution (IDE) is used to optimize the input weights and the hidden layer biases. The optimization is carried out according to the root mean squared error (RMSE) of the validation set and the norm of the output weights. Meantime, singular value decomposition (SVD)-based Reduced-dimensional Kalman (SVD-Kalman) filters are used to estimate the health parameters which can solve the problem of limited number of sensors in the model based diagnosis method. To improve the accuracy of fault diagnosis for an aero-engine component, the prediction results of health parameters based on both methods are fused by the improved recursive reduced least squares support vector regression (IRR-LSSVR). Simulation results show that compared with either the model-based or data-driven-based approach, the proposed fusion method improves the accuracy of fault diagnosis significantly.
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