TY - JOUR
T1 - 数据驱动的复杂产品智能服务技术与应用
AU - Li, Hao
AU - Wang, Haoqi
AU - Cheng, Ying
AU - Tao, Fei
AU - Hao, Bing
AU - Wang, Xinchang
AU - Ji, Yangjian
AU - Song, Wenyan
AU - Du, Wenliao
AU - Wen, Xiaoyu
AU - Gong, Xiaoyun
AU - Li, Ke
AU - Zhang, Yingfeng
AU - Luo, Guofu
AU - Li, Qifeng
N1 - Publisher Copyright:
© 2020, China Mechanical Engineering Magazine Office. All right reserved.
PY - 2020/4/10
Y1 - 2020/4/10
N2 - With the applications of sensors, data acquisition devices and other modules with sensing ability in the service operation stages of complex products, the operation and maintenance system of complex products was becoming more and more digital and intelligent. Data with real-time, multi-source, heterogeneous, and massive characteristics were become the basis of decision making to improve the reliability and low cost operation for complex product systems, especially the DT technology provided an effective way. Thus, the research progress of data-driven intelligent service for complex products was introduced. The characteristics and framework mode of data-driven intelligent services were analyzed herein, and data-driven intelligent service approaches for complex products were proposed, which included service-oriented complex product modeling and simulation methods, accurate analysis and prediction method for data-driven service demand acquisition, equipment fault identification and dynamic performance prediction based on DT, data-driven multi-objective decision-making optimization method for equipment condition-based maintenance and spare parts inventory, assisted maintenance technology for complex products based on DT, precise analysis and prediction method for energy efficiency of complex equipment based on multi-element cooperation, and optimal control method for complex product operation based on data mining. The application case of intelligent service system was given. The proposed framework and approach may provide reference for the intelligent transformation and upgrading of modern manufacturing services.
AB - With the applications of sensors, data acquisition devices and other modules with sensing ability in the service operation stages of complex products, the operation and maintenance system of complex products was becoming more and more digital and intelligent. Data with real-time, multi-source, heterogeneous, and massive characteristics were become the basis of decision making to improve the reliability and low cost operation for complex product systems, especially the DT technology provided an effective way. Thus, the research progress of data-driven intelligent service for complex products was introduced. The characteristics and framework mode of data-driven intelligent services were analyzed herein, and data-driven intelligent service approaches for complex products were proposed, which included service-oriented complex product modeling and simulation methods, accurate analysis and prediction method for data-driven service demand acquisition, equipment fault identification and dynamic performance prediction based on DT, data-driven multi-objective decision-making optimization method for equipment condition-based maintenance and spare parts inventory, assisted maintenance technology for complex products based on DT, precise analysis and prediction method for energy efficiency of complex equipment based on multi-element cooperation, and optimal control method for complex product operation based on data mining. The application case of intelligent service system was given. The proposed framework and approach may provide reference for the intelligent transformation and upgrading of modern manufacturing services.
KW - Data-driven
KW - Digital twin(DT)
KW - Intelligent manufacturing
KW - Intelligent service
UR - http://www.scopus.com/inward/record.url?scp=85086314062&partnerID=8YFLogxK
U2 - 10.3969/j.issn.1004-132X.2020.07.001
DO - 10.3969/j.issn.1004-132X.2020.07.001
M3 - 文章
AN - SCOPUS:85086314062
SN - 1004-132X
VL - 31
SP - 757
EP - 772
JO - Zhongguo Jixie Gongcheng/China Mechanical Engineering
JF - Zhongguo Jixie Gongcheng/China Mechanical Engineering
IS - 7
ER -