Dynamic neural network-based fault diagnosis of gas turbine engines
Dynamic neural network-based fault diagnosis of gas turbine engines
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DOI：
10.1016/j.neucom.2012.06.050
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发表时间：
2014-02
期刊：
影响因子：
6
通讯作者：
S. S. Tayarani-Bathaie-S.;Z. N. S. Vanini;K. Khorasani
中科院分区：
文献类型：
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作者：
S. S. Tayarani-Bathaie-S.;Z. N. S. Vanini;K. Khorasani
本文提出了一种基于神经网络的故障检测和隔离（FDI）方案，用于检测和隔离飞机喷气发动机高度非线性动力学中的故障。为此，首先开发了动态神经网络（DNN）来学习喷气发动机的输入输出图。 DNN 基于多层感知器网络构建，该网络使用 IIR（无限脉冲响应）滤波器在神经元的输入和输出之间生成动态，从而生成整个神经网络的动态。然后利用经过训练的动态神经网络来检测和隔离双转子涡轮风扇发动机中可能发生的部件故障。故障检测和隔离方案由多个 DNN 或并行滤波器组组成，对应于健康和故障发动机条件的各种运行模式。使用通过测量每个网络输出和测量的发动机输出的差异而产生的残差，建立各种标准来完成故障诊断任务，即解决系统组件的故障检测和隔离问题。进行了大量的仿真研究来证明和说明我们提出的故障诊断方案的优点、功能和性能。
In this paper, a neural network-based fault detection and isolation (FDI) scheme is presented to detect and isolate faults in a highly nonlinear dynamics of an aircraft jet engine. Towards this end, dynamic neural networks (DNN) are first developed to learn the input–output map of the jet engine. The DNN is constructed based on a multi-layer perceptron network which uses an IIR (infinite impulse response) filter to generate dynamics between the input and output of a neuron, and consequently of the entire neural network. The trained dynamic neural network is then utilized to detect and isolate component faults that may occur in a dual spool turbo fan engine. The fault detection and isolation schemes consist of multiple DNNs or parallel bank of filters, corresponding to various operating modes of the healthy and faulty engine conditions. Using the residuals that are generated by measuring the difference of each network output and the measured engine output various criteria are established for accomplishing the fault diagnosis task, that is addressing the problem of fault detection and isolation of the system components. A number of simulation studies are carried out to demonstrate and illustrate the advantages, capabilities, and performance of our proposed fault diagnosis scheme.