relation: https://khub.utp.edu.my/scholars/14398/
title: Deep�learning based prognosis approach for remaining useful life prediction of turbofan engine
creator: Muneer, A.
creator: Taib, S.M.
creator: Fati, S.M.
creator: Alhussian, H.
description: The entire life cycle of a turbofan engine is a type of asymmetrical process in which each engine part has different characteristics. Extracting and modeling the engine symmetry characteristics is significant in improving remaining useful life (RUL) predictions for aircraft components, and it is critical for an effective and reliable maintenance strategy. Such predictions can improve the maximum operating availability and reduce maintenance costs. Due to the high nonlinearity and complexity of mechanical systems, conventional methods are unable to satisfy the needs of medium-and long�term prediction problems and frequently overlook the effect of temporal information on prediction performance. To address this issue, this study presents a new attention�based deep convolutional neural network (DCNN) architecture to predict the RUL of turbofan engines. The prognosability metric was used for feature ranking and selection, whereas a time window method was employed for sample preparation to take advantage of multivariate temporal information for better feature extraction by means of an attention�based DCNN model. The validation of the proposed model was conducted using a well�known benchmark dataset and evaluation measures such as root mean square error (RMSE) and asymmetric scoring function (score) were used to validate the proposed approach. The experimental results show the superiority of the proposed approach to predict the RUL of a turbofan engine. The attention�based DCNN model achieved the best scores on the FD001 independent testing dataset, with an RMSE of 11.81 and a score of 223. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publisher: MDPI
date: 2021
type: Article
type: PeerReviewed
identifier:   Muneer, A. and Taib, S.M. and Fati, S.M. and Alhussian, H.  (2021) Deep�learning based prognosis approach for remaining useful life prediction of turbofan engine.  Symmetry, 13 (10).   ISSN 20738994     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116810999&doi=10.3390%2fsym13101861&partnerID=40&md5=eb41788eecb7c5bd8a261b58c0c28b45
relation: 10.3390/sym13101861
identifier: 10.3390/sym13101861