Fault Detection in Reaction Wheel of a Satellite Using Observer-Based Dynamic Neural Networks

2005 ◽  
pp. 584-590 ◽  
Author(s):  
Zhongqi Li ◽  
Liying Ma ◽  
Khashayar Khorasani
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Reaction Wheel ◽  
Dynamic Neural Networks

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Z. N. Sadough Vanini ◽  
N. Meskin ◽  
K. Khorasani
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Data ◽  
Turbine Engines ◽  
Multiple Model ◽  
Jet Engines ◽  
Dynamic Neural Networks ◽  
Autoassociative Neural Networks

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.

Hydraulic Looper Fault Detection and Isolation Using Dynamic Neural Networks

2004 ◽  
Vol 37 (15) ◽  
pp. 179-184
Author(s):  
Teodor Marciu ◽  
Birgit Köppen-Seliger ◽  
Reinhard Stücher
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Dynamic Neural Networks

Design of fault detection for a hydraulic looper using dynamic neural networks

2008 ◽  
Vol 16 (2) ◽  
pp. 192-213 ◽  
Author(s):  
Teodor Marcu ◽  
Birgit Köppen-Seliger ◽  
Reinhard Stücher
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Dynamic Neural Networks

Fault Diagnosis of Gas Turbine Engines by Using Dynamic Neural Networks

2010 ◽  
Author(s):  
Rasul Mohammadi ◽  
Esmaeil Naderi ◽  
Khashayar Khorasani ◽  
Shahin Hashtrudi-Zad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Detection ◽  
Gas Turbine ◽  
Network Architecture ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Dynamic Neural Networks ◽  
Feed Forward ◽  
The Difference

This paper presents a novel methodology for fault detection in gas turbine engines based on the concept of dynamic neural networks. The neural network structure belongs to the class of locally recurrent globally feed-forward networks. The architecture of the network is similar to the feed-forward multi-layer perceptron with the difference that the processing units include dynamic characteristics. The dynamics present in these networks make them a powerful tool useful for identification of nonlinear systems. The dynamic neural network architecture that is described in this paper is used for fault detection in a dual-spool turbo fan engine. A number of simulation studies are conducted to demonstrate and verify the advantages of our proposed neural network diagnosis methodology.

scholarly journals Fault Detection and Diagnosis for Non-Linear Processes empowered by Dynamic Neural Networks

2021 ◽  
pp. 107531
Author(s):  
Georgios Gravanis ◽  
Ioannis Dragogias ◽  
Konstantinos Papakiriakos ◽  
Chrysovalantou Ziogou ◽  
Konstantinos Diamantaras
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Linear Processes ◽  
Dynamic Neural Networks ◽  
Non Linear ◽  
Detection And Diagnosis

Distilling Knowledge From an Ensemble of Convolutional Neural Networks for Seismic Fault Detection

2020 ◽  
pp. 1-5
Author(s):  
Zirui Wang ◽  
Bo Li ◽  
Naihao Liu ◽  
Bangyu Wu ◽  
Xu Zhu
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Convolutional Neural Networks ◽  
Seismic Fault
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