Three-stage feature selection approach for deep learning-based RUL prediction methods

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dc.contributor.authorWang, Youdao
dc.contributor.authorZhao, Yifan
dc.date.accessioned2023-03-09T12:59:42Z
dc.date.available2023-03-09T12:59:42Z
dc.date.issued2023-02-27
dc.description.abstractThe remaining useful life (RUL) prediction plays an increasingly important role in predictive maintenance. With the development of big data and the Internet-of-Things (IoT), deep learning (DL) techniques have been widely adopted for RUL prediction. Addressing the limitation of the current methods for data under multiple operating conditions, this paper proposes a three-stage feature selection approach for DL-based RUL prediction models. The k-medoids cluster is initially used to sort raw data based on different operating conditions. In the first stage of feature selection, an operational-based normalisation approach is applied to reconstruct the data. Afterwards, Spearman's rank and pair-wise Pearson correlation coefficients are used to eliminate irrelevant and redundant features in the second and third stages, respectively. A case study using NASA's Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset is presented to quantitatively evaluate the influence of the proposed feature selection method using the Recurrent Neural Network (RNN) and its’ variants, enhanced by an optimised activation function and optimiser. The results confirm that the proposed method can improve the stability of DL models and achieve about a 7.3% average improvement in the RUL prediction for popular and state-of-the-art DL models.en_UK
dc.identifier.citationWang Y, Zhao Y. (2023) Three-stage feature selection approach for deep learning-based RUL prediction methods, Quality and Reliability Engineering International, Volume 39, Issue 4, June 2023, pp. 1223-1247en_UK
dc.identifier.eissn1099-1638
dc.identifier.issn0748-8017
dc.identifier.urihttps://doi.org/10.1002/qre.3288
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19277
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectdeep learningen_UK
dc.subjectfeature engineeringen_UK
dc.subjectlong short-term memoryen_UK
dc.subjectremaining useful life predictionen_UK
dc.titleThree-stage feature selection approach for deep learning-based RUL prediction methodsen_UK
dc.typeArticleen_UK

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