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المؤلفSadough Vanini, Z.N.
المؤلفMeskin, Nader
المؤلفKhorasani, K.
تاريخ الإتاحة2022-04-14T08:45:44Z
تاريخ النشر2014
اسم المنشورJournal of Engineering for Gas Turbines and Power
المصدرScopus
المعرّفhttp://dx.doi.org/10.1115/1.4027215
معرّف المصادر الموحدhttp://hdl.handle.net/10576/29821
الملخصIn 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.
اللغةen
الناشرAmerican Society of Mechanical Engineers
الموضوعJet 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
العنوانMultiple-model sensor and components fault diagnosis in gas turbine engines using autoassociative neural networks
النوعArticle
رقم العدد9
رقم المجلد136
dc.accessType Abstract Only


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