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AuthorKang, Ziqiu
AuthorCatal, Cagatay
AuthorTekinerdogan, Bedir
Available date2022-11-30T11:23:20Z
Publication Date2021
Publication NameSensors (Switzerland)
ResourceScopus
Resource2-s2.0-85100020823
URIhttp://dx.doi.org/10.3390/s21030932
URIhttp://hdl.handle.net/10576/36791
AbstractPredictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Languageen
PublisherMDPI
SubjectData mining; Machine learning; Maintenance prediction; Predictive maintenance; Production lines
TitleRemaining useful life (Rul) prediction of equipment in production lines using artificial neural networks
TypeArticle
Pagination20-Jan
Issue Number3
Volume Number21
dc.accessType Open Access


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