机械工程学报 >

2018 , Vol. 54 >Issue 22: 194 - 203

DOI: https://doi.org/10.3901/JME.2018.22.194

交叉与前沿

生命周期大数据驱动的复杂产品智能制造服务新模式研究

  • 任杉 ,
  • 张映锋 ,
  • 黄彬彬
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  • 西北工业大学现代设计与集成制造技术教育部重点实验室 西安 710072
任杉,男,1985年出生,博士研究生。主要研究方向为制造业大数据、产品生命周期管理、产品服务系统。E-mail:hhurs2010@163.com

收稿日期: 2017-07-05

  修回日期: 2017-12-06

  网络出版日期: 2018-11-20

基金资助

国家自然科学基金（51675441）和中央高校基本科研业务费专项资金（3102017jc04001）资助项目。

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New Pattern of Lifecycle Big-Data-Driven Smart Manufacturing Service for Complex Product

  • REN Shan ,
  • ZHANG Yingfeng ,
  • HUANG Binbin
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  • Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072

Received date: 2017-07-05

  Revised date: 2017-12-06

  Online published: 2018-11-20

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摘要

在分析当前制造企业产品生命周期管理面临的挑战与现有制造服务模式不足的基础上，针对复杂产品生命周期数据呈现的大数据特性，提出一种生命周期大数据驱动的复杂产品智能制造服务新模式，并设计了一种体系构架。以产品生命周期为主线，提出并详细阐述了生命周期大数据驱动的智能制造服务新模式的关键技术体系与实现框架，包括制造和运维动态数据驱动的产品与服务设计、实时多源数据驱动的生产过程分析与优化、面向服务的运维数据故障演化分析与预测等。通过上述体系构架及关键技术的实施，可有效促进生命周期各阶段数据和知识的集成应用，进而构建一种产品设计闭环创新、生产过程实时优化、运维服务动态预测的产品生命周期管理与运作机制，以提升全制造流程和全生命周期管理的智能决策能力。所提体系和框架可为生命周期大数据驱动的智能制造服务研究和应用提供一种参考模型。

关键词： 产品生命周期; 大数据; 智能制造; 产品设计; 运维服务

本文引用格式

任杉 , 张映锋 , 黄彬彬 . 生命周期大数据驱动的复杂产品智能制造服务新模式研究[J]. 机械工程学报, 2018 , 54(22) : 194 -203 . DOI: 10.3901/JME.2018.22.194

Abstract

According to the analysis of the current challenges of product lifecycle management (PLM) faced by manufacturing enterprise and the deficiencies of existing manufacturing service paradigms, a new pattern and holistic architecture of lifecycle big-data-driven smart manufacturing service (LCBD-SMS) for the complex product is firstly proposed and designed. Secondly,based upon the product lifecycle, the key technology system and implementation framework of the LCBD-SMS are put forward and elaborated, which included the product and service design based on dynamic production and operation data, the production process analysis and optimization based on real-time and multi-source data and the service-oriented fault evolution analysis and prediction, etc. Through implementation of the above-mentioned architecture and key technologies, the integrated application of the lifecycle data and knowledge can be facilitated effectively. Furthermore, a management and operation mechanism of product lifecycle characterized by the closed-loop and innovation of product design, the real-time optimization of the production process and the dynamic prediction of maintenance service are established. As a result, the ability of smart decision-making for the full manufacturing processes and the whole lifecycle management are enhanced. The proposed technology system and implementation framework could provide a reference model for LCBD-SMS research and application.

Key words： product lifecycle; big data; smart manufacturing; product design; maintenance and service

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