J
Jin Cui
Researcher at Beihang University
Publications - 24
Citations - 756
Jin Cui is an academic researcher from Beihang University. The author has contributed to research in topics: Cloud computing & Cloud manufacturing. The author has an hindex of 8, co-authored 21 publications receiving 465 citations. Previous affiliations of Jin Cui include Chinese Ministry of Education & Ningbo Institute of Technology, Zhejiang University.
Papers
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Bearing remaining useful life prediction based on deep autoencoder and deep neural networks
TL;DR: A novel eigenvector based on time–frequency-wavelet joint features is proposed to effectively represent bearing degradation process and a deep autoencoder based joint features compression and computing method is presented to retain effective information without increasing the scale of DNN.
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Multi-bearing remaining useful life collaborative prediction: A deep learning approach
TL;DR: An integrated deep learning approach for multi-bearing remaining useful life collaborative prediction by combining both time domain features and frequency domain features is proposed, which can extract high-quality degradation patterns of rolling bearing from vibration signals.
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Multi-scale Dense Gate Recurrent Unit Networks for bearing remaining useful life prediction
TL;DR: A novel deep learning network, namely Multi-scale Dense Gate Recurrent Unit Network (MDGRU) is proposed in this paper, which is composed of the feature layers initialized by pre-trained Restricted Boltzmann Machine (RBM) network, multi-scale layers, skip gate recurrent unit layers, dense layers.
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Pairwise comparison learning based bearing health quantitative modeling and its application in service life prediction
TL;DR: A learning-based health modeling method, on the basis of newly defined multidimensional frequency-domain health feature, is proposed to realize quantitative assessment of bearing health state.
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Research on the impact of service provider cooperative relationship on cloud manufacturing platform
TL;DR: In this paper, the authors proposed a CMfg platform evolution model (CPEM) to investigate the impacts of SPCR on the operational performance of a CMFG platform, and three quantitative metrics, from the perspective of cloud consumer, cloud operator, and cloud service provider, respectively, were established to support the performance evaluation.