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AuthorSadough Vanini, Z.N.
AuthorMeskin, Nader
AuthorKhorasani, K.
Available date2022-04-14T08:45:44Z
Publication Date2014
Publication NameJournal of Engineering for Gas Turbines and Power
ResourceScopus
Identifierhttp://dx.doi.org/10.1115/1.4027215
URIhttp://hdl.handle.net/10576/29821
AbstractIn 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. Copyright 2014 by ASME.
Languageen
PublisherAmerican Society of Mechanical Engineers
SubjectJet engines
Neural networks
Sensors
Aircraft jet engines
Autoassociative neural networks
Component fault detections
Dynamic neural networks
Fault detection and isolation schemes
Multiple-model approaches
Performance capability
Sensor data validation
Fault detection
TitleMultiple-model sensor and components fault diagnosis in gas turbine engines using autoassociative neural networks
TypeArticle
Issue Number9
Volume Number136
dc.accessType Abstract Only


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