Intelligent and Collaborative Assets for Industrial Manufacturing

工业制造的智能协作资产

• 批准号: 1949527
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1949527
• 关键词: Intelligent; Collaborative; Assets; Industrial; Manufacturing

Project Aim:To propose a feasible (applicable to the real-world manufacturing) methodology for an autonomous factory which will solve the efficiency, information visibility and availability problems in manufacturing and asset management.Project Objectives:Explore and exploit current concepts used in Industry 4.0 (i.e. Industrial Internet of Things (IIoT), Digital Twins, Cyber-Physical Systems (CPS), Edge-Cloud Computing, etc.).Identify the issues in applying current concepts in real-world and their effect on small and medium-sized enterprises (SMEs).Identify whether the hierarchical model of computer-integrated manufacturing is still suitable to be used with IIoT. If not, propose a new/ updated model.Design communication and system architectures which enable the assets to share their status with each other in order to achieve a given goal collaboratively.Identify and propose how to utilise the machine learning algorithms to provide predictive maintenance and analytical decisions to the assets.Identify and propose how to automatically compute the best strategy for the overall production line and the shop floor in unexpected situations (disruptions and failures), so the assets will autonomously handle the situation.Develop and evaluate a prototype autonomous factory which incorporates intelligent assets and intelligent processes.

项目目标：探索和利用工业4.0中使用的当前概念（即工业物联网（IIoT），数字孪生，网络物理系统（CPS），边缘云计算等），为自主工厂提出一种可行的（适用于现实世界的制造）方法，以解决制造和资产管理中的效率，信息可见性和可用性问题。确定当前概念在现实世界中应用时存在的问题及其对中小企业（SME）的影响。确定计算机集成制造的分层模型是否仍然适用于IIoT。如果没有的话，提出新的/更新模型。设计通信和系统架构，使资产能够彼此共享其状态，以便协作实现给定目标。确定并提出如何利用机器学习算法为资产提供预测性维护和分析决策。确定并提出如何自动计算整个生产线和车间的最佳策略，意外情况（中断和故障），因此资产将自主处理情况。开发和评估包含智能资产和智能流程的原型自主工厂。

项目成果

Using Expert Knowledge to Generate Data for Broadband Line Prognostics Under Limited Failure Data Availability

• DOI: 10.1016/j.ifacol.2020.11.043
• 发表时间: 2020
• 期刊: IFAC-PapersOnLine
• 作者: G. Ranasinghe;D. Yearling;M. Girolami;A. Parlikad

A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability

故障数据可用性有限条件下的预测方法

• DOI: 10.1109/access.2019.2960310
• 发表时间: 2019
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Ranasinghe G

Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability

在数据可用性有限的条件下生成用于预测的实值故障数据

• DOI: 10.1109/icphm.2019.8819392
• 发表时间: 2019
• 作者: Ranasinghe G

Collaborative prognostics in Social Asset Networks

• DOI: 10.1016/j.future.2018.02.011
• 发表时间: 2019-03
• 期刊: Future Gener. Comput. Syst.
• 作者: A. S. Palau;Zhenglin Liang;Daniel Lütgehetmann;A. Parlikad

Study of dynamic workload assignment strategies on production performance * *Acknowledgments to financial support of Cambridge Trust and China Scholarship Council

动态工作量分配策略对生产绩效的影响研究**感谢剑桥信托基金和国家留学基金委的资助

• DOI: 10.1016/j.ifacol.2017.08.2547
• 发表时间: 2017
• 期刊: IFAC-PapersOnLine
• 作者: Li H