TY  - JOUR
VL  - 90
AV  - none
Y1  - 2018///
KW  - Deterioration; Gases; Gaussian noise (electronic); Neural networks; Uncertainty analysis
KW  -  Component faults; Design/methodology/approach; Diagnostics techniques; Engine application; Fault identifications; Faults diagnostics; Gas path faults; Gas turbine engine; Neural networks structure; Structure-based
KW  -  Gas turbines
SP  - 992
PB  - Emerald Group Holdings Ltd.
A1  - Fentaye, A.D.
A1  - Baheta, A.T.
A1  - Gilani, S.I.U.-H.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057854050&doi=10.1108%2fAEAT-01-2018-0013&partnerID=40&md5=731ae23e0ab0329946d16f23cb288e87
EP  - 999
N2  - Purpose: The purpose of this paper is to present a quantitative fault diagnostic technique for a two-shaft gas turbine engine applications. Design/methodology/approach: Nested artificial neural networks (NANNs) were used to estimate the progressive deterioration of single and multiple gas-path components in terms of mass flow rate and isentropic efficiency indices. The data required to train and test this method are attained from a thermodynamic model of the engine under steady-state conditions. To evaluate the tolerance of the method against measurement uncertainties, Gaussian noise values were considered. Findings: The test results revealed that this proposed method is capable of quantifying single, double and triple component faults with a sufficiently high degree of accuracy. Moreover, the authors confirmed that NANNs have derivable advantages over the single structure-based methods available in the public domain, particularly over those designed to perform single and multiple faults together. Practical implications: This method can be used to assess engineâ??s health status to schedule its maintenance. Originality/value: For complicated gas turbine diagnostic problems, the conventional single artificial neural network (ANN) structure-based fault diagnostic technique may not be enough to get robust and accurate results. The diagnostic task can rather be better done if it is divided and shared with multiple neural network structures. The authors thus used seven decentralized ANN structures to assess seven different component fault scenarios, which enhances the fault identification accuracy significantly. © 2018, Emerald Publishing Limited.
ID  - scholars9874
N1  - cited By 10
TI  - Gas turbine gas-path fault identification using nested artificial neural networks
JF  - Aircraft Engineering and Aerospace Technology
IS  - 6
SN  - 17488842
ER  -