Y
Yue Meng
Researcher at Harbin Institute of Technology
Publications - 6
Citations - 331
Yue Meng is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Prognostics & Industry 4.0. The author has an hindex of 4, co-authored 4 publications receiving 201 citations.
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Journal ArticleDOI
Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance
TL;DR: A novel framework is proposed for structuring multisource heterogeneous information, characterizing structured data with consideration of the spatiotemporal property, and modeling invisible factors, which would make the production process transparent and eventually implement predictive maintenance on facilities and energy saving in the industry 4.0 era.
Proceedings ArticleDOI
Big-data-driven based intelligent prognostics scheme in industry 4.0 environment
TL;DR: The developed scheme demonstrated the important issues for the intelligent prognostics methodology, including pre-processing methods for industrial big data, association analysis based feature processing, and deep learning based progNostics model, spark platform based parallel computing, etc.
Journal ArticleDOI
Dynamic Genetic Algorithm-based Feature Selection Scheme for Machine Health Prognostics
Lei Lu,Jihong Yan,Yue Meng +2 more
TL;DR: Experimental results on predicting the lifetime of an unbalance vibration rotor system demonstrated that the proposed method can achieve better prognosis performance with less predicting errors.
Journal ArticleDOI
Shaft Orbit Feature Based Rotator Early Unbalance Fault Identification
Yue Meng,Lei Lu,Jihong Yan +2 more
TL;DR: Results shows that shaft orbit feature can be used in identifying different early fault stages of rotor unbalance, which indicates that utilizing shaft orbit as source of feature extraction can provide a new approach of getting early fault features in rotating machinery prognosis.
Journal ArticleDOI
Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques
TL;DR: In this article , a data-driven design and operation optimization network for turbomachinery is proposed, which can also present the physical field distributions, aiming at the turbine in a solar-based Supercritical Carbon Dioxide Brayton cycle.