Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Prediction Model of Working Hours of Cooling Turbine of Jet Engine with Back-propagation Neural Network

Ho-Sheng Chen, Tian-Syung Lan, and Yu-Ming Lai

(Received July 27, 2020; Accepted December 3, 2020)

Keywords: environmental control system, back-propagation neural network, prediction model, cooling turbine, airplane inspection

It is critical to maintain jet fighter aircraft so that they are in the best flying condition. Among the various components of aircraft, the environmental control system is crucial as it provides cool air for the cabin and electronic equipment. In this study, we develop a model that predicts the remaining working hours of the critical components of aircraft using a back-propagation neural network. First, we adopted the Delphi method with repeated questionnaire surveys to select six key components in the environmental control system, where the cooling turbine was considered the most important component for the prediction model. Then, with the same method, four of the key parameters for the cooling turbine were selected to establish the prediction model. Actual maintenance data from 2009 to 2013 was used for training the neural network to produce the smallest errors between the actual working hours according to inspection results and the predicted working hours. This verification result showed that the correlation and the goodness of fit were above 0.984 and 0.963, respectively, and the prediction accuracy was 93%. These results indicate that the prediction model with the back-propagation neural network can be effectively used to predict the remaining working hours of the cooling turbine. The model can reduce the cost of maintenance and the time lost from the disassembly of components and the halting of flights, as well as unnecessary wear and tear.

Corresponding author: Tian-Syung Lan




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