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首页> 外文期刊>Journal of Engineering for Gas Turbines and Power >Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks
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Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

机译：基于自动关联神经网络的燃气轮机多模型传感器及组件故障诊断

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摘要

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.
机译：本文采用基于智能的方法研究了飞机喷气发动机的故障诊断问题。所提出的故障检测和隔离（FDI）方案基于多模型方法，并利用了自动联想神经网络（AANN）。该方法由一组AANN组成，即使传感器和组件故障可能同时发生，也为传感器和组件故障检测与隔离问题提供了一种新颖的集成解决方案。此外，提出的算法可用于传感器数据的验证和校正，作为喷气发动机健康监测的第一步。我们还介绍了我们提出的方法与另一种称为动态神经网络的常用神经网络方案之间的比较，以证明我们方法的优势和能力。进行了各种仿真，以证明我们提出的故障检测和隔离方案的性能。

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  • 来源
    《Journal of Engineering for Gas Turbines and Power》 |2014年第9期|091603.1-091603.16|共16页
  • 作者

    Z. N. Sadough Vanini; N. Meskin; K. Khorasani;

  • 作者单位

    Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;

    Department of Electrical Engineering, Qatar University, Doha, Qatar;

    Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;

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