Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIE.2018.2844856
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dc.titleEstimation of bearing remaining useful life based on multiscale convolutional neural network
dc.contributor.authorZHU JUN
dc.contributor.authorCHEN NAN
dc.contributor.authorPENG WEIWEN
dc.date.accessioned2020-05-11T06:51:14Z
dc.date.available2020-05-11T06:51:14Z
dc.date.issued2018-06-22
dc.identifier.citationZHU JUN, CHEN NAN, PENG WEIWEN (2018-06-22). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics 66 (4) : 3208-3216. ScholarBank@NUS Repository. https://doi.org/10.1109/TIE.2018.2844856
dc.identifier.issn02780046
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/167946
dc.description.abstractBearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN). TFR can reveal nonstationary property of a bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs, which contain plenty of useful information using wavelet transform. Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning models. Here, we introduce an MSCNN model structure, which keeps the global and local information synchronously compared to a traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy.
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/8384285
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.typeArticle
dc.contributor.departmentDEPT OF INDUSTRIAL SYSTEMS ENGG & MGT
dc.description.doi10.1109/TIE.2018.2844856
dc.description.sourcetitleIEEE Transactions on Industrial Electronics
dc.description.volume66
dc.description.issue4
dc.description.page3208-3216
dc.published.statePublished
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