MATEC Web Conf.
Volume 314, 2020International Cross-Industry Safety Conference (ICSC) – International Symposium on Aircraft Technology, MRO and Operations (ISATECH) (ICSC-ISATECH 2019)
|Number of page(s)||15|
|Section||International Symposium on Aircraft Technology, MRO and Operations|
|Published online||29 May 2020|
An intelligent data filtering and fault detection method for gas turbine engines
School of Business, Society and Engineering, Mälardalen University, 883, SE-72123 Västerås, Sweden
* Corresponding author: firstname.lastname@example.org
In a gas turbine fault diagnostics, the removal of measurement noise and data outliers prior to the fault analysis is very essential. The conventional filtering methods, particularly the linear ones, are not sufficiently accurate, which might possibly lead to the loss of critically important features in the fault analysis process. Conversely, the recorded accuracies obtained from the non-linear filters are promising. Recently, the focus has been shifted to the artificial neural network (ANN) based nonlinear filters due to their capability of providing a robust identity map between the input and output data, which can be efficiently exploited in the process of fault diagnosis. This paper aims to present combined auto-associative neural network (AANN) and K-nearest neighbor (KNN) based noise reduction and fault detection method for a gas turbine engine application. The performance of the developed method has been evaluated using data obtained from a model simulation. The test results revealed that the developed hybrid method is more effective and reliable than the conventional methods for the fault detection of the gas turbine engine with negligible false alarms and missed detections.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.