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Li Yajing

Bio: Li Yajing is an academic researcher from The University of Nottingham Ningbo China. The author has contributed to research in topics: Bearing capacity & Turbocharger. The author has an hindex of 1, co-authored 2 publications receiving 16 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper breaks traditional procedures and presents a DT-based optimization strategy on the consideration of both machining efficiency and aerodynamic performance, as well as builds a reified 5-dimensional DT model.

51 citations

Patent
07 Nov 2017
TL;DR: In this article, a high-pulse-resisting large damping floating-type thrust bearing is proposed for a highpulse exhausting engine supercharging system to improve the reliability and service life of the supercharger.
Abstract: The invention discloses a high-pulse-resisting large damping floating-type thrust bearing. As for a traditional turbocharger thrust bearing, the emphasis attention aspects are the bearing capacity of the turbocharger thrust bearing and compression end oil leakage improvement of a supercharger, and therefore the turbocharger thrust bearing cannot adapt to the engine waste gas load impact working condition under high pulses. Compared with the traditional thrust bearing, the floating-type thrust bearing can achieve double-side bearing, so that the floating-type thrust bearing has large combined damping energy; two floating-type thrust bearings serve as a set to be installed on a turbocharger, and when the directions of axial force are different, double-side bearing can be achieved correspondingly; and under the axial force high-pulse impact working condition, the bearing capacity is large, the damping vibration absorption effect is good, the high-pulse-resisting large damping floating-type thrust bearing is suitable for a high-pulse exhausting engine supercharging system to a great extent, the reliability is improved, and the service life is prolonged.

Cited by
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Journal ArticleDOI
23 Sep 2021-Sensors
TL;DR: In this paper, a survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics.
Abstract: Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

54 citations

Journal ArticleDOI
TL;DR: A solution to existing challenging issues is proposed by introducing the digital twin (DT) technology into the OWT support structures and this new DT framework will enable real-time monitoring, fault diagnosis and operation optimization of the O WT support structures, which may provide a useful application prospect in the reliability analysis in the future.

51 citations

Journal ArticleDOI
TL;DR: Shortening product development cycles and fully customisable products pose major challenges for production systems as mentioned in this paper, and these not only have to cope with an increased product diversity but also enable h.....
Abstract: Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable h...

44 citations

Journal ArticleDOI
TL;DR: A multi-level cloud computing enabled digital twin system for the real-time monitor, decision and control of a synchronized production logistics system and the PLS optimization model of production and storage is presented with an industrial case, and the effectiveness is demonstrated and analyzed.

41 citations

Journal ArticleDOI
TL;DR: A technical system that embeds machine learning modules into digital twins and a combination of the digital twin in maintenance with machine learning in predictive maintenance of diesel locomotives is presented.
Abstract: The full life cycle management of complex equipment is considered fundamental to the intelligent transformation and upgrading of the modern manufacturing industry. Digital twin technology and machine learning have been emerging technologies in recent years. The application of these two technologies in the full life cycle management of complex equipment can make each stage of the life cycle more responsive, predictable, and adaptable. This paper first proposes a technical system that embeds machine learning modules into digital twins. Next, on this basis, a full life cycle digital twin for complex equipment is constructed, and joint application of sub-models and machine learning is explored. Then, the application of a combination of the digital twin in maintenance with machine learning in predictive maintenance of diesel locomotives is presented. The effectiveness of the proposed management method is verified by experiments. The abnormal axle temperature can be alarmed about one week in advance. Lastly, possible application advantages of the combination of digital twin and machine learning in addressing future research direction in this field are introduced.

26 citations