运维数据与多模态知识驱动的复杂产品改进设计新模式研究

doi:10.3901/JME.2025.03.091

机械工程学报 ›› 2025, Vol. 61 ›› Issue (3): 91-104.doi: 10.3901/JME.2025.03.091

• 特邀专栏：人机联合认知赋能的高端装备设计、制造与运维 • 上一篇

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运维数据与多模态知识驱动的复杂产品改进设计新模式研究

赵欣¹, 任杉², 张耿¹, 张映锋¹

1. 西北工业大学工业工程与智能制造工信部重点实验室西安 710072;
2. 西安邮电大学现代邮政学院西安 710061

收稿日期:2024-03-04 修回日期:2024-06-18 发布日期:2025-03-12
作者简介:赵欣，女，1984年出生，博士研究生。主要研究方向为产品智能设计与运维、知识图谱与产品生命周期管理。E-mail:zhaoxin_ie@mail.nwpu.edu.cn;任杉，男，1985年出生，博士，副教授，硕士研究生导师。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:renshan@xupt.edu.cn;张耿，男，1991年出生，博士，副教授，硕士研究生导师。主要研究方向为智能制造系统、工业物联驱动的智能生产、制造资源服务运行优化。E-mail:geng.zhang@nwpu.edu.cn;张映锋(通信作者)，男，1979年出生，博士，教授，博士研究生导师。主要研究方向为物联制造系统、产品服务系统与绿色制造、制造系统智能化。E-mail:zhangyf@nwpu.edu.cn
基金资助:
NSFC-广东联合基金(U2001201)和国家自然科学基金(52305554)资助项目。

New Pattern of Operation & Maintenance Data and Multi-modal Knowledge Driven Improvement Design for Complex Products

ZHAO Xin¹, REN Shan², ZHANG Geng¹, ZHANG Yingfeng¹

1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing of Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an 710072;
2. School of Modern Post, Xi'an University of Posts and Telecommunications, Xi'an 710061

Received:2024-03-04 Revised:2024-06-18 Published:2025-03-12

摘要/Abstract

摘要： 在分析当前复杂产品制造企业创新发展面临的挑战以及现有产品改进设计方法局限性与设计模式演进方向的基础上，结合复杂产品各生命周期阶段业务决策过程呈现的数据与知识联合驱动的特征，提出一种运维数据与多模态知识驱动的复杂产品改进设计新模式，设计了相应的体系架构，并讨论了其“配置→监控→评估→关联→反馈→改进”运行逻辑，进而提出该模式下的关键技术体系，包括复杂产品运维过程数据主动感知与生命周期数据增值计算、复杂产品关键功能模块性能退化评估与关键设计参数识别、面向复杂产品改进设计的知识建模与设计方案智能配置等。上述技术的实施，可促进复杂产品设计与运维跨阶段、多业务的协同优化，进而提升产品改进设计方案的有效性和方案配置的智能化。所提模式与技术体系对探索引领高端装备制造业发展的制造服务新模式的研究和应用有重要借鉴价值。

关键词: 运维数据, 多模态知识, 改进设计, 产品生命周期

Abstract: Based on analysis of the challenges facing by complex products (CPs) manufacturing enterprises during innovative development, the limitations of existing product improvement design methods and the evolution directions of current product design paradigms, a new pattern and architecture of operation & maintenance (O&M) data and multi-modal knowledge (MMK) driven improvement design for CPs is proposed and designed. The operation logic of "configuration → monitoring → evaluation → correlation → feedback → improvement" for the proposed pattern is discussed. The related key technologies of the new pattern are put forward and elaborated, which included the O&M data proactive perceiving and lifecycle data value-adding calculation of CPs, the key functional modules performance degradation assessment and key design parameters identification of CPs, and the knowledge modeling and design scheme intelligent configuration for CPs improvement design, etc. By applying these technologies, the cross-stage and multi-business collaborative optimization of design and O&M stages can be facilitated effectively. As a result, the effectiveness of product improvement design schemes and the intelligence of scheme configuration processes are enhanced. The proposed new pattern and key technologies could provide important referential value to the research and application of exploring a new manufacturing service mode that leading the development of high-end equipment manufacturing industry.

Key words: operation and maintenance data, multi-modal knowledge, improving design, product lifecycle

中图分类号:

TH186
TH166

赵欣, 任杉, 张耿, 张映锋. 运维数据与多模态知识驱动的复杂产品改进设计新模式研究[J]. 机械工程学报, 2025, 61(3): 91-104.

ZHAO Xin, REN Shan, ZHANG Geng, ZHANG Yingfeng. New Pattern of Operation & Maintenance Data and Multi-modal Knowledge Driven Improvement Design for Complex Products[J]. Journal of Mechanical Engineering, 2025, 61(3): 91-104.

参考文献

[1] 谭建荣，刘振宇，徐敬华. 新一代人工智能引领下的智能产品与装备[J]. 中国工程科学，2018，20(4):35-43. TAN Jianrong，LIU Zhenyu，XU Jinghua. Intelligent products and equipment led by new-generation artificial intelligence[J]. Strategic Study of CAE，2018，20(4):35-43.
[2] 周济. 智能制造——“中国制造2025”的主攻方向[J]. 中国机械工程，2015，26(17):2273-2284. ZHOU Ji. Intelligent manufacturing——main direction of “Made in China 2025”[J]. China Mechanical Engineering，2015，26(17):2273-2284.
[3] 王旭，李文川. 制造业的新理念——闭环产品生命周期管理[J]. 中国机械工程，2010，21(14):1687-1693. WANG Xu，LI Wenchuan. New concept for manufacturing industry——closed-loop product lifecycle management[J]. China Mechanical Engineering，2010，21(14):1687-1693.
[4] 苗田，张旭，熊辉，等. 数字孪生技术在产品生命周期中的应用与展望[J]. 计算机集成制造系统，2019，25(6):1546-1558. MIAO Tian，ZHANG Xu，XIONG Hui，et al. Applications and expectation of digital twin in product lifecycle[J]. Computer Integrated Manufacturing System，2019，25(6):1546-1558.
[5] 李浩，陶飞，王昊琪，等. 基于数字孪生的复杂产品设计制造一体化开发框架与关键技术[J]. 计算机集成制造系统，2019，25(6):1320-1336. LI Hao，TAO Fei，WANG Haoqi，et al. Integration framework and key technologies of complex product design-manufacturing based on digital twin[J]. Computer Integrated Manufacturing System，2019，25(6):1320-1336.
[6] REN Shan，ZHANG Yingfeng，LIU Yang，et al. A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing:A framework, challenges and future research directions[J]. Journal of Cleaner Production，2019，210:1343-1365.
[7] 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. REN Shan，ZHANG Yingfeng，HUANG Binbin. New pattern of lifecycle big-data-driven smart manufacturing service for complex product[J]. Journal of Mechanical Engineering，2018，54(22):194-203.
[8] TAO Fei，SUI Fangyuan，LIU Ang，et al. Digital twin-driven product design framework[J]. International Journal of Production Research，2019，57(12):3935-3953.
[9] 洪兆溪，冯毅雄，娄山河，等. 复杂产品不确定性智能设计研究综述与展望[J]. 机械工程学报，2023，59(19):213-236. HONG Zhaoxi，FENG Yixiong，LOU Shanhe，et al. Overview and prospects of uncertain intelligent design for complex products[J]. Journal of Mechanical Engineering，2023，59(19):213-236.
[10] 张光立. 基于设计意图的产品设计方案生成与优选研究[D]. 合肥:合肥工业大学，2022. ZHANG Guangli. Research on product design scheme generation and optimization based on design intention[D]. Hefei:Hefei University of Technology，2022.
[11] 彭珍瑞，张楠，殷红，等. 基于频响函数的动车组构架传感器优化布置[J]. 西南交通大学学报，2019，54(2):402-407，414. PENG Zhenrui，ZHANG Nan，YIN Hong，et al. Optimal sensor placement of emu frame based on frequency response function[J]. Journal of Southwest Jiaotong University，2019，54(2):402-407，414.
[12] 史书慧. 无人机电动舵机运行状态感知优化设计方法研究[D]. 哈尔滨:哈尔滨工业大学，2020. SHI Shuhui. Sensing optimization design of UAV electric actuator operation state[D]. Harbin:Harbin Institute of Technology，2020.
[13] WU Jie，ZI Yangyang，MA Hongru，et al. Optimal placement of sensors based on data fusion for condition monitoring of pulley group under speed variation condition[J]. Machines，2022，10(2):148.
[14] 刘俊. 动车组关键部件数据处理方法研究与实现[D]. 北京:北京交通大学，2019. LIU Jun. Research and implementation of data processing methods for key components of EMU[D]. Beijing:Beijing Jiaotong University，2019.
[15] WEI Yupeng，WU Dazhong，TERPENNY J. Decision-level data fusion in quality control and predictive maintenance[J]. IEEE Transactions on Automation Science and Engineering，2021，18(1):184–194.
[16] SAADALLAH A，FINKELDEY F，BUSS J，et al. Simulation and sensor data fusion for machine learning application[J]. Advanced Engineering Informatics，2022，52:101600.
[17] 尹爱军，张智禹，李海珠. 同步抽取变换与复小波结构相似性指数的滚动轴承性能退化评估[J]. 振动与冲击，2020，39(6):205-209. YIN Aijun，ZHANG Zhiyu，LI Haizhu. Rolling bearing performance degradation assessment based on the synchroextracting transform and complex wavelet structural similarity index[J]. Journal of Vibration and Shock，2020，39(6):205-209.
[18] DONG Shuzhi，WEN Guangrui，LEI Zihao，et al. Transfer learning for bearing performance degradation assessment based on deep hierarchical features[J]. ISA Transactions，2021，108:343-355.
[19] 王庆锋，刘家赫，刘晓金，等. 数据驱动的旋转设备性能退化趋势预测方法[J]. 计算机集成制造系统，2022，28(3):724-734. WANG Qingfeng，LIU Jiahe，LIU Xiaojin，et al. Data driven performance degradation trend predicting method for the rotating equipment[J]. Computer Integrated Manufacturing System，2022，28(3):724-734.
[20] 马斌彬. 基于性能退化分析的风电机组关键设计参数识别[D]. 上海:上海交通大学，2020. MA Binbin. Identification of critical parameters based on wind turbines performance degradation[D]. Shanghai:Shanghai Jiao Tong University，2020.
[21] 褚学宁，陈汉斯，马红占. 性能数据驱动的机械产品关键设计参数识别方法[J]. 机械工程学报，2021，57(3):185-196. CHU Xuening，CHEN Hansi，MA Hongzhan. Identification of critical design parameter for mechanical products based on performance data[J]. Journal of Mechanical Engineering，2021，57(3):185-196.
[22] WANG Chuncai，YE Chang，BI Yanru，et al. Application of mechanical product design parameter optimization based on machine learning in identification[J]. Production Planning & Control，2023，1-15.
[23] FANG Jun，WEI Xing. A knowledge support approach for the preliminary design of platform-based products in Engineering-To-Order manufacturing[J]. Advanced Engineering Informatics，2020，46:101196.
[24] 梁野，裘乐淼，刘晓健，等. 基于设计情境与脑机反馈融合的产品设计知识需求感知技术[J]. 机械工程学报，2020，56(7):151-163. LIANG Ye，QIU Lemiao，LIU Xiaojian，et al. Technology for product design knowledge need awareness based on design context and brain-computer interface[J]. Journal of Mechanical Engineering，2020，56(7):151-163.
[25] ZHENG Hao，YANG Shang，LOU Shanhe，et al. Knowledge-based integrated product design framework towards sustainable low-carbon manufacturing[J]. Advanced Engineering Informatics，2021，48:101258.
[26] JI Yongjun，JIANG Zuhua，LI Xinyu，et al. A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design[J]. Journal of Intelligent Manufacturing，2023，34(4):1615–1637.
[27] ZHAO Shuangyao，ZHANG Qiang，PENG Zhanglin，et al. Integrating customer requirements into customized product configuration design based on Kano’s model[J]. Journal of Intelligent Manufacturing，2020，31(3):597–613.
[28] GUO Yuming. Towards the efficient generation of variant design in product development networks: Network nodes importance based product configuration evaluation approach[J]. Journal of Intelligent Manufacturing，2023，34(2):615–631.
[29] 付岩，王黎明，李方义，等. 基于FSMP模型的机电产品绿色设计方案生成方法[J]. 计算机集成制造系统，2023，29(4):1301-1312. FU Yan，WANG Liming，LI Fangyi，et al. Generation method of green design scheme for mechatronic products based on FSMP mode[J]. Computer Integrated Manufacturing System，2023，29(4):1301-1312.
[30] WANG Zhi，LI Hanxiong，CHEN Chunlin. Reinforcement learning-based optimal sensor placement for spatiotemporal modeling[J]. IEEE Transactions on Cybernetics，2020，50(6):2861-2871.
[31] AZIN M，SONG Mingming，BABAK M，et al. Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost[J]. Mechanical Systems and Signal Processing，2022，169:108787.
[32] ZHANG Jian，SIMEONE A，PENG Qingjin，et al. Dependency and correlation analysis of specifications and parameters of products for supporting design decisions[J]. CIRP Annals-Manufacturing Technology，2020，69(1):133-136.
[33] CHEN Zhihua，ZHOU Tongtong，MING Xinguo，et al. Configuration optimization of service solution for smart product service system under hybrid uncertain environments[J]. Advanced Engineering Informatics，2022，52:101632.
[34] LIANG Dong，REN Mengyang，XIANG Zhongxia，et al. A novel smart product-service system configuration method for mass personalization based on knowledge graph[J]. Journal of Cleaner Production，2023，382:135270.
[35] 屈挺，张凯，闫勉，等. 物联网环境下面向高动态性生产系统优态运行的联动决策与控制方法[J]. 机械工程学报，2018，54(16):24-33. QU Ting，ZHANG Kai，YAN Mian，et al. Synchronized decision-making and control method for opti-state execution of dynamic production systems with Internet of Things[J]. Journal of Mechanical Engineering，2018，54(16):24-33.
[36] GUO Zhengang，ZHANG Yingfeng，LIU Sichao，et al. Exploring self-organization and self-adaption for smart manufacturing complex networks[J]. Frontiers of Engineering Management，2022，10(2):206-222.
[37] TAO Fei，CHENg Ying，XU Lida，et al. CCIoT-CMfg: Cloud computing and internet of things-based cloud manufacturing service system[J]. IEEE Transactions on Industrial Informatics，2014，10(2):1435-1442.
[38] ZHU Shiqiang，TING Yu，XU Tao，et al. Intelligent computing:the latest advances, challenges, and future[J]. Intelligent Computing，2023，2:0006.
[39] 任杉. 产品生命周期大数据驱动的设计-运维集成服务方法研究[D]. 西安:西北工业大学，2019. REN Shan. Product lifecycle big data-driven approach of the integrated service for design & operation and maintenance[D]. Xi’an:Northwestern Polytechnical University，2019.

运维数据与多模态知识驱动的复杂产品改进设计新模式研究

New Pattern of Operation & Maintenance Data and Multi-modal Knowledge Driven Improvement Design for Complex Products

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[3]	刘飞;李聪波;曹华军;王秋莲. 基于产品生命周期主线的绿色制造技术内涵及技术体系框架[J]. , 2009, 45(12): 115-120.
[4]	郭伟;王凤岐;杜玉明;胡素梅. 产品生命周期需求的分析及其间映射方法的研究[J]. , 1998, 34(5): 40-48.