scholarly journals Fault Detection and Isolation Based on Neural Networks Case Study: Steam Turbine

2011 ◽  
Vol 03 (04) ◽  
pp. 513-516 ◽  
Author(s):  
D. Benazzouz ◽  
S. Benammar ◽  
S. Adjerid
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Steam Turbine ◽  
Fault Detection And Isolation

Miscellaneous Neural Networks Applied to Fault Detection and Isolation of an Evaporation Station

2000 ◽  
Vol 33 (11) ◽  
pp. 359-364 ◽  
Author(s):  
Teodor Marcu ◽  
Letitia Mireat ◽  
Lavinia Ferariu ◽  
M. Paul Frank
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Fault Detection And Isolation

scholarly journals Toward Threat of Implementation Attacks on Substation Security: Case Study on Fault Detection and Isolation

2018 ◽  
Vol 14 (6) ◽  
pp. 2442-2451 ◽  
Author(s):  
Anupam Chattopadhyay ◽  
Abhisek Ukil ◽  
Dirmanto Jap ◽  
Shivam Bhasin
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation

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.

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