智能压力容器:从运维到全寿命周期的安全智能技术

轩福贞, 杨斌, 贾国栋, 赵云妹, 赵鹏, 宫建国, 王海杰, 邵珊珊

特种设备学报 ›› 2026, Vol. 1 ›› Issue (1) : 36-63.

特种设备学报 ›› 2026, Vol. 1 ›› Issue (1) : 36-63. DOI: 10.27022/j.issn2097-7697.2026.01.004
专题综述

智能压力容器:从运维到全寿命周期的安全智能技术

  • 轩福贞2, 杨斌1, 贾国栋3, 赵云妹1, 赵鹏2, 宫建国2, 王海杰2, 邵珊珊3
作者信息 +

Intelligent Pressure Vessels: Safety Intelligence Technologies from Operation and Maintenance to Full Life Cycle

  • XUAN Fuzhen2, YANG Bin1, JIA Guodong3, ZHAO Yunmei1, ZHAO Peng2, GONG Jianguo2, WANG Haijie2, SHAO Shanshan3
Author information +
文章历史 +

摘要

压力容器技术的发展历程体现了工业文明的演进。我国重大工程领域装备具有运行参数苛刻化、结构尺寸大型化、服役环境复杂化等典型特征,这对从强度设计、制造质量到运维的全寿命周期压力容器安全技术提出了全新挑战与要求,而人工智能的发展为压力容器安全智能技术的更替提供了前所未有的契机。本文首先结合压力容器的服役工况、损伤模式及运行安全保障的传统方法,综述了压力容器的基本支撑——强度与安全;其次,从新型传感方法及其适用性、全局与局部状态监测方法、多源传感监测方法4个方面总结了面向运维安全的压力容器状态监测技术的新进展;再次,结合数据科学的发展,从强度寿命的智能化预测、状态的智能化评估、数字孪生与系统的自主演进3个维度分析了面向全寿命周期的压力容器智能化技术,并引入系统工程思想,构建了一个涵盖L1状态感知、L2诊断预警、L3预测维护、L4协同决策和L5自主优化的5级智能化水平框架,明确了压力容器智能技术的演进路径,阐明了其在压力容器安全智能技术进程中的推动作用;最后,提出了智能压力容器的概念,并总结了其面临的挑战与对策,进一步展望了机械强度信息学对发展智能压力容器的重要意义。

Abstract

The evolution of pressure vessel technology reflects the progress of industrial civilization. In major engineering applications in China, equipment is characterized by increasingly severe operating parameters, large structural dimensions, and complex service environments. These features impose new challenges and requirements on safety technologies of pressure vessels throughout the entire life cycle, covering strength design, manufacturing quality, and operation and maintenance. Meanwhile, the rapid development of artificial intelligence offers an unprecedented opportunity for upgrading intelligent safety technologies of pressure vessels. This paper reviews the traditional approaches for ensuring operation safety by analyzing the service conditions, damage mechanisms, and conventional assessment methods of pressure vessels, highlighting strength and safety as their fundamental supports. Recent advances in condition monitoring technologies for operational safety are summarized, including novel sensing methods, applicability evaluation, global and local state monitoring, and multi-source sensing fusion. With the integration of data science, intelligent technologies for life-cycle management of pressure vessels are further discussed from the perspectives of strength-life prediction, intelligent evaluation, and autonomous digital twin evolution, elucidating their promoting role in the progress of intelligent safety technologies. The concept of an intelligent pressure vessel is then proposed, and the challenges and countermeasures in its implementation are outlined. The importance of mechanical strength informatics in advancing intelligent pressure vessels is finally emphasized.

关键词

智能压力容器 / 全寿命周期 / 安全智能 / 机械强度信息学

Key words

Intelligent pressure vessel / Life cycle / Safety intelligence / Mechanical strength informatics

引用本文

导出引用
轩福贞, 杨斌, 贾国栋, 赵云妹, 赵鹏, 宫建国, 王海杰, 邵珊珊. 智能压力容器:从运维到全寿命周期的安全智能技术[J]. 特种设备学报, 2026, 1(1): 36-63. https://doi.org/10.27022/j.issn2097-7697.2026.01.004
XUAN Fuzhen, YANG Bin, JIA Guodong, ZHAO Yunmei, ZHAO Peng, GONG Jianguo, WANG Haijie, SHAO Shanshan. Intelligent Pressure Vessels: Safety Intelligence Technologies from Operation and Maintenance to Full Life Cycle[J].Journal of Special Equipment, 2026, 1(1): 36-63. https://doi.org/10.27022/j.issn2097-7697.2026.01.004
中图分类号: X924.2   

参考文献

[1] TSG 21—2016 固定式压力容器安全技术监察规程[S].
[2] 郑津洋,桑芝富.过程设备设计:压力容器[M].北京:化学工业出版社,2025.
[3] 曹峰. 极端工况下化工设备压力容器安全性能研究[J]. 广州化工, 2025, 53(16): 187-189.
[4] 郭鹏, 奥志城. 海洋油气压力容器完整性检测体系研究[J]. 化工管理, 2025(26):1-3.
[5] WHITNEY H L.Evolution of pressure vessels to meet present-day refining pressures and temperatures[J]. Journal of Fluids Engineering, 1932, 54(9): 27-30.
[6] THOMSON J R.Learning from ignorance: A brief history of pressure vessel integrity and failures[M]. Amsterdam: Elsevier, 2015: 99-125.
[7] SPENCE J, NASH D H.Milestones in pressure vessel technology[J]. International Journal of Pressure Vessels and Piping, 2004, 81(2): 89-118.
[8] CHEN P, MA J.Analysis of pressure vessel manufacturing technology development[J]. Applied Mechanics and Materials, 2010, 42: 160-165.
[9] ZHU G H, SHAH M.Steel composite structural pressure vessel technology: Future development analysis of worldwide important pressure vessel technology[J]. Process Safety Progress, 2004, 23(1): 65-71.
[10] 蒋小文, 杜侠鸣, 齐一华. 浅谈我国压力容器技术发展及其趋势[J]. 化工设备与管道, 2022, 59(1): 8-15.
[11] 陈学东, 范志超, 崔军, 等. 我国压力容器高性能制造技术进展[J]. 压力容器, 2021, 38(10): 1-15.
[12] 陈学东, 范志超, 陈永东, 等. 我国压力容器设计制造与维护的绿色化与智能化[J]. 压力容器, 2017, 34(11): 12-27.
[13] WANG M K, LUO Z A, XIE G M, et al. Effect of heating temperature on microstructure and mechanical properties of titanium clad Cr-Mo pressure vessel steel by vacuum rolling cladding[C]//AIP Conference Proceedings, 2022, 2 474(1): 020 017.
[14] 章轩, 王宇飞, 王昊, 等. 航天无内衬复合材料压力容器结构设计与制备[J]. 宇航材料工艺, 2025, 55(4): 1-10.
[15] 刘雁鹏, 任中杰, 韩宇泽, 等. 超薄单层复合材料缠绕压力容器承载性能研究[J]. 压力容器, 2024, 41(1): 45-52.
[16] 曾鑫, 谈建平, 任千一, 等. 国内压水堆核电厂反应堆压力容器的概率断裂分析与讨论[J]. 原子能科学技术, 2025, 59(6):1 343-1 351.
[17] 蒋鑫, 张博, 乔小丽, 等. 固定式高压气态储氢压力容器发展与现状[J]. 太阳能学报, 2025,46(6):110-119.
[18] 顾然. 快开门式压力容器安全联锁装置的联锁路径及联锁逻辑[J]. 特种设备安全技术, 2025(5):11-13.
[19] 周晓光. 特种设备锅炉压力容器检验的问题研究[J]. 中国标准化, 2024(14):181-184.
[20] 张超鹏, 陈波, 张虎, 等. 航天大型压力容器水压强度试验控制系统研究[J]. 机床与液压, 2025,53(17):135-140.
[21] 李继峰, 王博, 黄志影. 特殊结构压力容器设计温度的探讨[J]. 石油化工设备技术, 2024,45(2):15-18.
[22] 孙星. 特种设备压力容器定期检测安全技术研究[J]. 现代职业安全, 2025(2):94-96.
[23] 李彬楠. 试论化工设备压力容器规范设计及发展[J]. 当代化工研究, 2023(17):147-149.
[24] ASME BPVC.VIII.1—2023 ASME boiler and pressure vessel code, Section VIII: Rules for construction of pressure vessels—Division 1[S].
[25] EN 13445 Unfired pressure vessels[S].
[26] API RP 580 Elements of a risk-based inspection program[S].
[27] 何玉坤. 锅炉压力容器检验中的常见问题分析[J]. 模具制造, 2025, 25(9):225-227.
[28] 陈飞飞. 机器人在压力容器检验检测中的应用[J]. 中国高新科技, 2024(1):37-39.
[29] 宋利滨, 韩志远, 谢国山. 国内外压力容器失效率调查研究进展及数据对比分析[J]. 化工设备与管道,2025, 62(3):104-110.
[30] 曹法洲, 丁一. 热处理技术在石油化工压力容器中的应用研究[J].中国石油和化工标准与质量, 2020,40(13):219-220.
[31] 高峰, 蔡勤, 陈韶斌, 等. 换热压力容器在线能效测试及监测系统[R].珠海:广东省特种设备检测研究院珠海检测院, 2020.
[32] 卢锡铭. 化工压力容器腐蚀影响因素及防腐措施探讨[J]. 石化技术,2025,32(10):370-372.
[33] 孙学超. 压力容器在化工设备安装中的关键技术[J].上海塑料, 2025,53(2):61-66.
[34] 周智伟, 姚雨, 杨秀昌, 等. 极端海洋环境下海油平台压力容器抗腐修复技术研究[J].中国石油和化工标准与质量, 2025,45(16):19-21.
[35] 许瑞杰, 雷娜, 唐宇光, 等. 海上平台压力容器内件改造优化设计[J]. 石化技术, 2025,32(5):139-141.
[36] 周明. 压力容器设计制造常见缺陷及应对措施[J]. 设备管理与维修, 2021(22):133-135.
[37] 王丹丹, 周金秀. 压力容器设计制造中的常见问题探析[J].石化技术,2019,26(10):350-351.
[38] 谭琇遥. 压力容器设计及制造常见问题浅析[J]. 中国石油和化工标准与质量, 2020,40(24):26-28.
[39] 欧阳白胜. 压力容器在特殊工况下的设计、制造与检验[J]. 造纸装备及材料, 2024,53(12):45-47.
[40] 段瑞, 元少昀. 关于压力容器设计使用年限的探讨[J]. 石油化工设备技术, 2025,46(1): 5-6.
[41] 郭东伟, 孙丽. 金属压力容器旋制成形工艺及工艺参数优化设计[J]. 山西冶金, 2025,48(8):129-131.
[42] 杨明. 浅谈压力容器制造监督检验工作中的要点[J]. 产品可靠性报告, 2025(8):150-151.
[43] 曹野, 赵明. 热处理技术在压力容器设计中的应用[J]. 石化技术, 2016,23(10):129.
[44] 边缘, 折文裕, 蒋剑超. 低温压力容器定期检验难点剖析与应对策略研究[J]. 化工设计通讯, 2025,51(7):44-46.
[45] 刘立群, 孙远霞. 涡流成像无损检测在压力容器的应用[J].一重技术,2025(3):37-39.
[46] 孙海洋. 低频导波检测技术在压力容器检验中的应用[J]. 品牌与标准化, 2025(2):121-123.
[47] 王铎, 申正. 声发射技术在压力容器检验中的应用[J]. 石化技术, 2025,32(8):149-151.
[48] 蔡婧. 压力容器设计制造常见缺陷与措施分析[J]. 中国设备工程, 2025(13):131-133.
[49] 张贺博, 谢晓东, 何雨宸. 压力容器定期检验方法的综合应用研究及与人工智能融合应用的展望[J].中国设备工程, 2025(13):185-187.
[50] 陈亚楠, 周双龙, 李顺彪, 等. 压力容器运维过程中无损检测的应用研究[J]. 清洗世界, 2024,40(3):181-183.
[51] 陈振伟. 化工压力容器的设计、制造及检验检测中的质量监督控制[J]. 山东化工, 2024,53(14):221-223.
[52] KATAM R, PASUPULETI V D K, KALAPATAPU P. A review on structural health monitoring: Past to present[J]. Innovative Infrastructure Solutions, 2023,8:248.
[53] LUQUE J, STRAUB D.Risk-based optimal inspection strategies for structural systems using dynamic Bayesian networks[J]. Structural Safety, 2019,76:68-80.
[54] MEI T, TONG C Z, TONG B R, et al.Mapping the knowledge domain of pressure vessels and piping fields for safety research in industrial processes: A bibliometric analysis[J]. Processes, 2025,13(1):74.
[55] 胡鑫懿. 云制造背景下基于知识图谱的压力容器智能设计计算方法[D]. 杭州: 浙江工业大学, 2021.
[56] 陈小功. 基于数字孪生的高温高压复合材料压力容器寿命预测技术研究[J]. 消费电子, 2025(2):251-253.
[57] 黎蓉, 陈诚. 人工智能与计算流体力学在压力容器设计中的应用[J]. 石油化工设计, 2021,38(2):50-53.
[58] 于继凯, 周国发, 欧可升. 齿啮式快开门压力容器协同智能优化设计研究[J]. 化工机械, 2012,39(3):356-360.
[59] 陈继华, 江燕云, 黄建华, 等. 智能全站仪在大型压力容器焊接变形监测中的应用[J]. 测绘科学技术学报, 2008(3):224-227.
[60] 孙岩, 苏明. 浅析压力容器智能制造中的质量控制信息化[J].中国设备工程,2025(3):30-33.
[61] 谢云, 魏玉鹏, 包建军, 等. 重型压力容器焊接数字化车间研究与实践应用[J].数字化转型, 2025,2(3):104-111.
[62] 段彪王, 程静静, 张卫刚. 人工智能在压力容器无损检测中的应用[J]. 科技创新与应用, 2025,15(24):193-196.
[63] 黄翔. 压力容器定期检验智能化展望[J]. 特种设备安全技术, 2023(1):14-16.
[64] 叶晓明, 任彦斌, 陈选其. 大数据与人工智能在油田压力容器中的融合应用[J].中国特种设备安全, 2025,41(2):81-85.
[65] 蔡康健, 徐巍, 郭新然, 等. 物联网技术在瓶式高压储氢容器损伤监测中的应用[J]. 物联网技术,2024,14(11):7-8.
[66] 王超. 物联网在移动式压力容器充装中的应用[J]. 石油化工设备技术, 2025,46(3):36-40.
[67] 刘三江, 陈祖志, 李光海. 智能网联特种设备使用管理模式分析—— 以移动式压力容器为例[J]. 中国特种设备安全,2020,36(12):1-6.
[68] 杨帆, 江波. 物联网技术在化工设备检测与维护管理中的应用—— 评《化工设备维护与检修》[J]. 化学工程, 2025, 53(2):前插8.
[69] 段军军, 贺杠, 刘艳雄. 化工设备压力容器破坏原因及预防分析[J]. 中国设备工程, 2024(1):196-198.
[70] U.S. DOE.A technology roadmap for Generation IV nuclear energy systems[R]. Washington D.C.: Nuclear Energy Research Advisory Committee and the Generation IV International Forum, 2002.
[71] GB/T 30579—2022 承压设备损伤模式识别[S].
[72] ISO 16528-1:2024 Boilers and pressure vessels—Part 1: Performance requirements[S].
[73] ASME BPVC.VIII.2—2023 ASME boiler and pressure vessel code, Section VIII: Rules for construction of pressure vessels—Division 2: Alternative rules[S].
[74] GB/T4732—2024 压力容器分析设计[S].
[75] ASME BPVC.V—2023 ASME boiler and pressure vessel code, Section V: Nondestructive examination[S].
[76] R5. Assessment procedure for the high temperature response of structures[S]. London:EDF Energy,2014.
[77] R6. Assessment of the integrity of structures containing defects[S]. London:British Energy,2001.
[78] HU C J, YANG B, YANG L L, et al.Anti-interference damage localization in composite overwrapped pressure vessels using machine learning and ultrasonic guided waves[J]. NDT & E International, 2023,140: 102 961.
[79] CHEN X D, YANG T C, FAN Z C, et al.On-line monitoring and warning of important in-service pressure equipment based on characteristic safety parameters[C]//Pressure Vessels and Piping Conference. New York: American Society of Mechanical Engineers, 2017: V01BTA038.
[80] FAZLE A B, PRODHAN R K, ISLAM M M.AI-powered predictive failure analysis in pressure vessels using real-time sensor fusion: Enhancing industrial safety and infrastructure reliability[J]. American Journal of Scholarly Research and Innovation, 2023,2(2): 102-134.
[81] KARAPANAGIOTIS C, SCHUKAR M, BREITHAUPT M, et al.Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing[C]// Proceedings of the 11th European Workshop on Structural Health Monitoring. Germany: NDT. net,2024.
[82] BOUHALA L, POLESEL J, KARATRANTOS A, et al.Review of state-of-the-art of structural health monitoring in hydrogen composite pressure vessels[J]. Composites Part C: Open Access, 2025,18:100 635.
[83] SABATO A, DABETWAR S, KULKARNI N N, et al.Noncontact sensing techniques for AI-aided structural health monitoring: A systematic review[J]. IEEE Sensors Journal, 2023,23(5):4 672-4 684.
[84] LI W B, LYU H, ZHANG L J, et al.Experiment, simulation, optimization design, and damage detection of composite shell of hydrogen storage vessel—A review[J]. Journal of Reinforced Plastics and Composites, 2022, 42(11-12): 507-536.
[85] PIRAINO F, PAGNOTTA L, CORIGLIANO O, et al.Advances in type IV tanks for safe hydrogen storage: materials, technologies and challenges[J]. Hydrogen, 2025,6(4):80.
[86] BHARDWAJ S, DHINGRA S.A detailed review on flexible pressure sensors using strain gauge[J].International Journal of Novel Research and Development,2023,8(9):e236-e238.
[87] YANG M, PENG K L, LI Z, et al.Recent progress in flexible materials for wearable devices for body function and athletic performance monitoring[J]. Chemical Engineering Journal, 2025,505:159 659.
[88] SHI J D, LI X M, CHENG H Y, et al.Graphene reinforced carbon nanotube networks for wearable strain sensors[J]. Advanced Functional Materials, 2016, 26(13):2 078-2 084.
[89] XU H C, ZHENG W H, WANG Y J, et al.Flexible tensile strain-pressure sensor with an off-axis deformation-insensitivity[J]. Nano Energy, 2022,99:107 384.
[90] SONG D K, LI X F, LI X P, et al.Hollow-structured MXene-PDMS composites as flexible, wearable and highly bendable sensors with wide working range[J]. Journal of Colloid and Interface Science, 2019,555: 751-758.
[91] AMJADI M, KYUNG K U, PARK I, et al.Stretchable, skin-mountable, and wearable strain sensors and their potential applications:A review[J]. Advanced Functional Materials, 2016, 26(11):1 678-1 698.
[92] WANG X Y, LIM E G, HOETTGES K, et al.A review of carbon nanotubes, graphene and nanodiamond based strain sensor in harsh environments[J].C-Journal of Carbon Research,2023,9(4):108.
[93] JIANG T H, WANG C N, LING T Y, et al.Recent advances and new frontiers of flexible pressure sensors: structure engineering, performance and applications[J]. Materials Today Physics, 2024, 48: 101 576.
[94] CHEN Y J, LIANG M S, TIAN M Y, et al.Flexible circuits engineered for complex and extreme environments[J]. Soft Science, 2025,556.
[95] ASLAM Z, ZHANG H, SREEJITH V S, et al.Advances in the surface acoustic wave sensors for industrial applications: Potentials, challenges and future directions:A review[J]. Measurement, 2023, 222:113 657.
[96] XUE T, XU F M, TAN Q T, et al.LGS-based SAW sensor that can measurement pressure up to 1 000 °C[J]. Sensors and Actuators A: Physical, 2022,334: 113 315.
[97] MIAO H L, BI J L, GAO Y, et al.A microstructure-enhanced dual-mode LC sensor with a PSO-BP algorithm for precise detection of temperature and pressure[J]. Advanced Functional Materials, 2024,34(48).
[98] 徐静宁, 蔡飞达, 张勇斌, 等. 无线无源声表面波传感器温度补偿方法[J]. 电子元件与材料, 2025, 44(1):41-48.
[99] CHENG H T, EBADI S, GONG X.A low-profile wireless passive temperature sensor using resonator/antenna integration up to 1 000 °C[J]. IEEE Antennas and Wireless Propagation Letters, 2012, 11: 369-372.
[100] LIU X, ZHANG H L, BIAN K G, et al.Meta-backscatter: A new ISAC paradigm for battery-free Internet of Things[J]. IEEE Communications Magazine, 2024, 62(9):106-112.
[101] ZHANG J, SUNNY A L, ZHANG G, et al.Feature extraction for robust crack monitoring using passive wireless RFID antenna sensors[J]. IEEE Sensors Journal, 2018,18(15):6 273-6 280.
[102] SCHAECHTLE T, AFTAB T, REINDL L M, et al.Wireless passive sensor technology through electrically conductive media over an acoustic channel[J]. Sensors, 2023, 23(4):2 043.
[103] TSENG V F, BEDAIR S S, LAZARUS N.Acoustic power transfer and communication with a wireless sensor embedded within metal[J]. IEEE Sensors Journal, 2018, 18(13): 5 550-5 558.
[104] ALLAM A, PATEL H, SUGINO C, et al.Portable through-metal ultrasonic power transfer using a dry-coupled detachable transmitter[J]. Ultrasonics, 2024, 141:107 339.
[105] SEPETCIOGLU H, TARAKCIOGLU N.Effect of graphene nanoplatelets on progressive failure behavior under internal pressure of composite cylindrical pressure vessels[J]. Journal of Materials Engineering and Performance, 2022,31(1):2 225-2 239.
[106] LIN L Y, WANG X Q, YANG B, et al.Condition monitoring of composite overwrap pressure vessels based on buckypaper and MXene sensors[J]. Composites Communications, 2021,25:100 699.
[107] YU Y H, LIU X, LI J, et al.Life-cycle health monitoring of composite structures using piezoelectric sensor network[J]. Smart Materials and Structures, 2022, 31(1): 015 033.
[108] QING X L, LIU X, ZHU J J, et al.In-situ monitoring of liquid composite molding process using piezoelectric sensor network[J]. Structural Health Monitoring, 2020, 20(5): 2 840-2 852.
[109] CAPINERI L, BULLETTI A.Ultrasonic guided-waves sensors and integrated structural health monitoring systems for impact detection and localization: A review[J]. Sensors, 2021,21(9):2 929.
[110] SOMAN R, WEE J, PETERS K.Optical fiber sensors for ultrasonic structural health monitoring: A review[J]. Sensors, 2021,21(21):7 345.
[111] BRUNNER A J.Structural health and condition monitoring with acoustic emission and guided ultrasonic waves: What about long-term durability of sensors, sensor coupling and measurement chain?[J]. Applied Sciences, 2021,11(24):11 648.
[112] HASSANI S, DACKERMANN U.A systematic review of advanced sensor technologies for non-destructive testing and structural health monitoring[J]. Sensors, 2023,23(4):2 204.
[113] ZHOU W, WANG J, PAN Z B, et al.Review on optimization design, failure analysis and non-destructive testing of composite hydrogen storage vessel[J]. International Journal of Hydrogen Energy, 2022, 47(91): 38 862-38 883.
[114] HU C J, FU X L, YUAN Y W, et al.A novel damage localization technique for type III composite pressure vessels based on guided wave mode-matching method[J]. Composite Structures, 2024, 346:118 414.
[115] HASSANI S, MOUSAVI M, GANDOMI A H.Structural health monitoring in composite structures: A comprehensive review[J]. Sensors, 2022,22(1):153.
[116] ANGELOPOULOS N.Damage detection and damage evolution monitoring of composite materials for naval applications using acoustic emission testing[D]. Birmingham: University of Birmingham, 2017.
[117] ROMHÁNY G, CZIGÁNY T, KARGER-KOCSIS J. Failure assessment and evaluation of damage development and crack growth in polymer composites via localization of acoustic emission events:A review[J]. Polymer Reviews, 2017,57(3):397-439.
[118] WANG D L, LIAO B B, HAO C Y, et al.Acoustic emission characteristics of used 70 MPa type IV hydrogen storage tanks during hydrostatic burst tests[J]. International Journal of Hydrogen Energy, 2021,46(23):12 605-12 614.
[119] GÜEMES A, FERNANDEZ-LOPEZ A, POZO A R, et al. Structural health monitoring for advanced composite structures: A review[J].Journal of Composites Science, 2020,4(1):65.
[120] CHOU H Y, MOURITZ A P, BANNISTER M K, et al.Acoustic emission analysis of composite pressure vessels under constant and cyclic pressure[J]. Composites Part A: Applied Science and Manufacturing, 2015,70:111-120.
[121] ANASTASOPOULOS A, KOUROUSIS D, COLE P.Acoustic emission inspection of spherical metallic pressure vessels[C]// 2nd International Conference on Technical Inspection and NDT. Tehran, Iran, 2008.
[122] WURITI G, CHATTOPADHYAYA S, THOMAS T.Acoustic emission test method for investigation of M250 maraging steel pressure vessels for aerospace applications[J]. Materials Today: Proceedings, 2022, 49: 2 176-2 182.
[123] LIAO B B, WANG D L, HAMDI M, et al.Acoustic emission-based damage characterization of 70 MPa type IV hydrogen composite pressure vessels during hydraulic tests[J]. International Journal of Hydrogen Energy, 2019, 44(40): 22 494-22 506.
[124] JIANG P, LIU X D, LI W, et al.Damage characterization of carbon fiber composite pressure vessels based on modal acoustic emission[J]. Materials, 2022,15(14):4 783.
[125] REN X Y, WANG J, LIANG Y J, et al.Acoustic emission detection of filament wound CFRP composite structure damage based on Mel spectrogram and deep learning[J]. Thin-Walled Structures, 2024,198:111 683.
[126] WANG J H, ZHENG C Z, QIU J, et al.An optimized VMD-wavelet denoising method for leakage detection in water supply networks from acoustic emission signals[J]. International Journal of Pressure Vessels and Piping, 2025,217:105 535.
[127] CUI J L, QU X Q, LYU C, et al.Multi-parameter acoustic emission analysis for fatigue crack evaluation in structural health monitoring[J]. Measurement,2025,256:118 529.
[128] HAO J Y, JU S, DUAN K, et al.A damage assessment method based on recent-time acoustic emission events for composite interlaminar damage[J]. Results in Engineering, 2025,27:106 016.
[129] LI F C, LIU Z Q, SUN X W, et al.Propagation of guided waves in pressure vessel[J]. Wave Motion, 2015,52: 216-228.
[130] LYU F, ZHOU X Y, DING Z, et al.Application research of ultrasonic-guided wave technology in pipeline corrosion defect detection: A review[J]. Coatings, 2024,14(3):358.
[131] MEMMOLO V, MAIO L, RICCI F.Assessment of damage in composite pressure vessels using guided waves[J]. Sensors, 2022,22(14):5 182.
[132] LUGOVTSOVA Y, PRAGER J.Structural health monitoring of composite pressure vessels using guided ultrasonic waves[J]. Insight-Non-Destructive Testing and Condition Monitoring, 2018,60(3): 139-144.
[133] ZHAO J L, YANG L H, WANG H Y, et al.Laser-generated guided waves for damage detection in metal-lined composite-overwrapped pressure vessels[J]. Polymers, 2022,14(18):3 823.
[134] CROXFORD A J, WILCOX P D, DRINKWATER B W, et al.Strategies for guided-wave structural health monitoring[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2007, 463(2 087):2 961-2 981.
[135] SOMAN R, KUDELA P, BALASUBRAMANIAM K, et al.A study of sensor placement optimization problem for guided wave-based damage detection[J]. Sensors, 2019,19(8):1 856.
[136] SOMAN R.Multi-objective optimization for joint actuator and sensor placement for guided waves based structural health monitoring using fibre bragg grating sensors[J]. Ultrasonics, 2022,119:106 605.
[137] MOGHADAM P Y, QUAEGEBEUR N, MASSON P.Design and optimization of a multi-element piezoelectric transducer for mode-selective generation of guided waves[J]. Smart Materials and Structures, 2016,25(7):075 037.
[138] ZHANG H, YANG Z F, LYU S, et al.Damage identification method based on ultrasonic guided wave sensor network and path optimization Bayesian fusion algorithm[J]. IEEE Sensors Journal, 2024,24(6): 8 661-8 673.
[139] SOMAN R, MALINOWSKI P. A real-valued genetic algorithm for optimization of sensor placement for guided wave-based structural health monitoring[J]. Journal of Sensors, 2019, 2019(1): 9 614 630.
[140] ROSE J L.Ultrasonic guided waves in solid media[M]. Cambridge: Cambridge University Press, 2014.
[141] JOSEPH R, YU L Y, GIURGIUTIU V. Excitation and propagation of guided waves in multilayer hollow cylinders using PWAS transducers: a theoretical and experimental study[J]. Journal of Acoustics, 2020,2: e200 003.
[142] ZHANG Z, PAN H, WANG X Y, et al.Deep learning empowered structural health monitoring and damage diagnostics for structures with weldment via decoding ultrasonic guided wave[J]. Sensors, 2022,22(14):5 390.
[143] SHANG L, ZHANG Z, TANG F J, et al.Signal process of ultrasonic guided wave for damage detection of localized defects in plates: From shallow learning to deep learning[J]. Journal of Data Science and Intelligent Systems, 2025,3(2):149-164.
[144] WANG X C, LIN M, LI J, et al.Ultrasonic guided wave imaging with deep learning: applications in corrosion mapping[J]. Mechanical Systems and Signal Processing, 2022,169:108 761.
[145] LIU Q, WANG Y, ZHAO F J, et al.A review of the research progress of sensor monitoring technology in harsh engineering environments[J]. Sensors, 2025, 25(20):6 308.
[146] HU C J, YANG B, YAN J J, et al.Damage localization in pressure vessel by guided waves based on convolution neural network approach[J]. Journal of Pressure Vessel Technology, 2020,142(6):061 601.
[147] GAO X, QI Q H, QUMU M, et al.An arc-shaped magnetostrictive guided wave array transducer and rotational synthetic aperture focusing technology imaging method[J]. Measurement, 2025,258:119 254.
[148] ZHOU S L, SHEN Y F, WANG C Q, et al.Self-sensing piezoelectric composites via generation and reception of ultrasonic guided waves[J]. Composite Structures, 2025,357:118 927.
[149] BRAHEM N, KHALIFA A B, LAGACHE M, et al.Experimental investigation of single and butt-welded GFRP filament winding tubes bending behavior based on conventional and digital image correlation method[J]. Thin-Walled Structures, 2023,191:111 073.
[150] MENG L B, JIN G C, YAO X F, et al.3D full-field deformation monitoring of fiber composite pressure vessel using 3D digital speckle correlation method[J]. Polymer Testing, 2006,25(1):42-48.
[151] PAN B, WU D F, WANG Z Y, et al.High-temperature digital image correlation method for full-field deformation measurement at 1 200 °C[J]. Measurement Science and Technology, 2011, 22(1):015 701.
[152] XU W, FENG X, LI J, et al.Strain analysis of pressure vessels contained pits based on digital image correlation method[C]// Seventh International Symposium on Precision Mechanical Measurements. Xiamen:[s.n.],2015:990 310.
[153] THOSTENSON E T, CHOU T W.Carbon nanotube networks: Sensing of distributed strain and damage for life prediction and self-healing[J]. Advanced Materials, 2006,18(21):2 837-2 841.
[154] AVILÉS F, MAY-PAT A, LÓPEZ-MANCHADO M A, et al. A comparative study on the mechanical, electrical and piezoresistive properties of polymer composites using carbon nanostructures of different topology[J]. European Polymer Journal,2018,99: 394-402.
[155] ZHANG H, LIU Y, KUWATA M, et al.Improved fracture toughness and integrated damage sensing capability by spraycoated CNTs on carbon fibre prepreg[J]. Composites Part A: Applied Science and Manufacturing, 2015,70:102-110.
[156] NAUMAN S.Piezoresistive sensing approaches for structural health monitoring of polymer composites—A review[J]. Eng, 2021,2(2):197-226.
[157] XIAO B, YANG B, XUAN F Z, et al.In-situ monitoring of a filament wound pressure vessel by the MWCNT sensor under hydraulic fatigue cycling and pressurization[J]. Sensors, 2019,19(6):1 396.
[158] ZHANG L, QU X Q, ZHAO Z P, et al.Health monitoring of composite pressure vessels through omnidirectional buckypaper sensor array[J]. Applied Physics A, 2022, 128:178.
[159] WANG X Q, LIN L Y, LU S W, et al.Evaluation of embedded buckypaper sensors in composite overwrapped pressure vessels for progressive damage monitoring[J]. Composite Structures, 2022,284:115 223.
[160] WANG Y, SHAN J W, WENG G J.Percolation threshold and electrical conductivity of graphene-based nanocomposites with filler agglomeration and interfacial tunneling[J]. Journal of Applied Physics, 2015,118(6):065 101.
[161] WANG Y F, WANG K, ZHANG C.Applications of artificial intelligence/machine learning to high-performance composites[J]. Composites Part B: Engineering, 2024,285:111 740.
[162] MUSA S M A, DZULKIFLI M H, AZMI A I, et al. Embedded and surface-mounted fiber Bragg grating as a multiparameter sensor in fiber-reinforced polymer composite materials: A review[J]. IEEE Access, 2023, 11: 86 611-86 644.
[163] LUNG B C.A structural health monitoring system for composite pressure vessels[J]. Composite Structures, 2024,312:117-899.
[164] ZHOU K, WU Z Y.Strain gauge placement optimization for structural performance assessment[J]. Engineering Structures, 2017,141:184-197.
[165] SIRETA F X, STORHAUG G.A modal approach for holistic hull structure monitoring from strain gauges measurements and structural analysis[C]// Offshore Technology Conference. 2022:2 514-2 523.
[166] YOON J, LEE J, KIM G, et al.Deep neural network-based structural health monitoring technique for real-time crack detection and localization using strain gauge sensors[J]. Scientific Reports,2022,12:20 204.
[167] ARAMBURU A B, CRUZ J A D, SILVA A A X D, et al. Non-destructive testing techniques for pressure vessels manufactured with polymer composite materials: a systematic review[J]. Measurement, 2025, 246:116 729.
[168] LINTS M, SALUPERE A, SANTOS S D.Simulation of detecting contact nonlinearity in carbon fibre polymer using ultrasonic nonlinear delayed time reversal[J]. Acta Acustica, 2017,103(6):978-986.
[169] FU L L, WU J H, YANG J S, et al.Laser ultrasonic damage identification of composites based on empirical mode decomposition and neural network[J]. Optics and Lasers in Engineering, 2024,181:108 397.
[170] QIAN C, QU Y Q, CHEN N, et al.Noncontact evaluation of steel mechanical properties using nonlinear laser ultrasonics[J]. NDT & E International, 2025,156:103 445.
[171] KARAPANAGIOTIS C, SCHUKAR M, KREBBER K, et al.Distributed fiber optic sensors for structural health monitoring of composite pressure vessels[J]. Technisches Messen, 2024,91(3-4):168-179.
[172] LIU Q X, CHEN H F, ANTON B, et al.Progress in application on health monitoring technology for aerospace composite structures[J]. Acta Materiae Compositae Sinica, 2024,41(9):4 563-4 588.
[173] KANG D H, KIM C U, KIM C G.The embedment of fiber Bragg grating sensors into filament wound pressure tanks considering multiplexing[J]. NDT & E International, 2006,39(2):109-116.
[174] 沃江海. 高精度干涉型光纤传感器的理论与实验研究[D]. 武汉: 华中科技大学, 2014.
[175] NI C H, ZHAO M, WANG X H, et al.Research on the application of interferometric optical fiber sensors in high-pressure gas pipelines[J]. Optical Fiber Technology, 2024,87:103 862.
[176] 杨波, 彭俊毅. 光纤光栅传感器在现代大型飞机中的应用探讨[C]// 大型飞机关键技术高层论坛暨中国航空学会2007年年会论文集.深圳:中国航空学会,2007:531-535.
[177] ZHU W S, QIN Z B, GUO J F, et al.Research on stress monitoring method of pressure vessels based on fiber Bragg grating[C]// Proceedings of the 5th International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2025).Xi'an: SPIE,2025:136 942B.
[178] LIU C, LIU Z Q, WANG P P, et al.Flexible pressure sensor realized by a chirped fiber Bragg grating pair for vital signs monitoring[J]. Journal of Lightwave Technology, 2025, 43(10):4 778-4 786.
[179] 吕佩珏, 黄哲, 王晓明, 等. 多功能传感器集成综述[J]. 电子与封装, 2023, 23(8):5-16.
[180] 张永军, 刘小龙, 罗炳海. 多智能体协作定位研究综述[J]. 现代导航, 2025, 16(3):157-161.
[181] 王帅达, 林冠英, 王暖升, 等. 基于机器学习的智能传感器综述[J]. 电子技术应用, 2025, 51(3):32-38.
[182] 王海颖. 多源数据关联与融合算法研究[D]. 无锡: 江南大学, 2016.
[183] 姜延吉. 多传感器数据融合关键技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2010.
[184] 冯成. 多源异构数据融合关键技术研究[D]. 北京: 北京邮电大学, 2020.
[185] Office for Nuclear Regulation. AP1000 UK Generic Design Assessment: Step 4 Assessment Report for Structural Integrity (ONR GDA SR-11-002 Rev.0) [R]. Bootle: Office for Nuclear Regulation, 2011.
[186] 高景宏, 赵杰, 李明原, 等. 面向精准医疗的多源异构数据融合技术研究[J]. 医学信息学杂志, 2021, 42(5): 69-74.
[187] 贺雅琪. 多源异构数据融合关键技术研究及其应用[D]. 成都: 电子科技大学, 2018.
[188] 胡笑旋. 贝叶斯网建模技术及其在决策中的应用[D]. 合肥: 合肥工业大学, 2006.
[189] 田锦毅. 基于容积卡尔曼滤波的动态指向式旋转导向钻具系统的传感器故障检测[D]. 青岛: 中国石油大学(华东), 2022.
[190] WANG C X, ELSAYED E A, LI K, et al.Multisensor degradation data fusion and remaining life prediction[C]// Proceedings of the ASME Pressure Vessels and Piping Conference, Volume 5: High-Pressure Technology; ASME Nondestructive Evaluation, Diagnosis and Prognosis Division. Waikoloa, Hawaii, USA:ASME, 2017: V005T10A003.
[191] 宁云晖, 田盛丰, 宁培泰. 应用证据理论(D-S方法)解多传感器数据融合问题[J]. 系统工程与电子技术, 2001(3):98-101.
[192] 韩静, 陶云刚. 基于D-S证据理论和模糊数学的多传感器数据融合算法[J]. 仪器仪表学报,2000(6):644-647.
[193] 王其昂, 刘泉, 马占国, 等. 基于稀疏贝叶斯学习与改进D-S证据理论多源数据融合的结构性能评估[J]. 工程力学,2025:1-9.
[194] 张燕. 基于深度学习与D-S理论的多模态数据特征融合算法[J].吉林大学学报(理学版), 2025,63(3):855-860.
[195] 张育智, 李乔, 单德山. 基于BP网络与模糊积分的结构损伤识别[C]// 第十八届全国桥梁学术会议论文集(下册).天津:中国土木工程学会,2008:1 421-1 426.
[196] 王璇, 李春升, 周荫清. 多传感器信息融合技术[J]. 北京航空航天大学学报, 1994(4):402-406.
[197] SUN R R, REN Y M.A multi-source heterogeneous data fusion method for intelligent systems in the Internet of Things[J]. Intelligent Systems with Applications, 2024, 23: 200 424.
[198] MA X P, ZHOU P M, HE X X, et al.A comprehensive review of multi-source data fusion processing methods[J]. Journal of Intelligent Systems, 2025,17(2):215-230.
[199] 宋奎勇. 面向试验数据的多源信息融合方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2023.
[200] WANG H D, YIN C, BAI Y C, et al.Multi-agent-based multi-source information fusion processing method for pressure equipment operation and maintenance[C]// IEEE International Conference on Information, Big Data and Artificial Intelligence (ICIBA).USA:IEEE, 2024: 10 869 018.
[201] 刘宏宇. 基于TUFusion的无人机可见光与红外融合检测算法研究[J]. 软件, 2025,46(8):39-43.
[202] 胥守振. 基于声学扫描振镜的超声/光声双模态成像技术研究[D]. 成都: 电子科技大学,2022.
[203] 王成彬, 马小刚, 陈建国. 数据预处理技术在地学大数据中应用[J]. 岩石学报, 2018, 34(2):303-313.
[204] 吴一全,蔡佳琦.自动驾驶中深度学习的三维目标检测方法综述[J/OL].智能系统学报,1-23[2026-01-20].https://link.cnki.net/urlid/23.1538.tp.20250818.1757.002.
[205] 李庚松, 刘艺, 郑奇斌, 等. 无人机多传感器数据融合研究综述[J]. 软件学报, 2025,36(4):1 881-1 905.
[206] 吴方伟, 石砦, 吴毅, 等. 多传感器融合的火灾检测算法综述[J]. 数字技术与应用, 2024,42(12):204-207.
[207] 黄书童, 贾晓丽. 多传感器数据融合技术在管道无损检测中的应用[J]. 无损检测, 2024,46(4):69-73.
[208] 杜梦迪. 近海底多源数据集成及其在海洋工程中的应用[D].青岛:中国科学院大学(中国科学院海洋研究所), 2023.
[209] 刘玉斌. 声光集成探测技术在深海油气工程中的应用[D].青岛:中国科学院大学(中国科学院海洋研究所), 2023.
[210] 周媛. 基于数据驱动的航空发动机状态监测关键技术研究[D]. 南京: 南京航空航天大学, 2015.
[211] BARBOSA J F, CORREIA J A, JÚNIOR R F, et al. Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network[J]. International Journal of Fatigue, 2020,135:105 527.
[212] CHEN J, LIU Y M.Fatigue modeling using neural networks: A comprehensive review[J]. Fatigue & Fracture of Engineering Materials & Structures, 2022,45(4):945-979.
[213] HU H T, QI L H, CHAO X J.Physics-informed neural networks (PINN) for computational solid mechanics: Numerical frameworks and applications[J]. Thin-Walled Structures, 2024, 205: 112 495.
[214] GAN L, WU H, ZHONG Z.Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine[J]. International Journal of Fatigue, 2022,158:106 761.
[215] KALAYCI C B, KARAGOZ S, KARAKAS Ö.Soft computing methods for fatigue life estimation: A review of the current state and future trends[J]. Fatigue & Fracture of Engineering Materials & Structures, 2020,43(12):2 763-2 785.
[216] ABIODUN O I, JANTAN A, OMOLARA A E, et al. State-of-the-art in artificial neural network applications: a survey[J]. Heliyon, 2018,4(11):e00 938.
[217] LI Z X, GOEBEL K, WU D Z.Degradation modeling and remaining useful life prediction of aircraft engines using ensemble learning[J]. Journal of Engineering for Gas Turbines and Power, 2019,141: 041 008.
[218] GAN L, WU H, ZHONG Z.On the integration of domain knowledge and branching neural network for fatigue life prediction with small samples[J]. International Journal of Fatigue, 2023,172:107 648.
[219] GAO J, LI J, LI J, et al.Research progress on thermal aging of reactor pressure vessel steel[J]. Materials Science and Technology, 2025,41:459-487.
[220] AL-STOUHI S, REDDY C K.Transfer learning for class imbalance problems with inadequate data[J]. Knowledge and Information Systems, 2016,48(1):201-228.
[221] HAN T, LIU C, WU R, et al.Deep transfer learning with limited data for machinery fault diagnosis[J]. Applied Soft Computing, 2021,103:107 150.
[222] CANDEMIR S, NGUYEN X V, FOLIO L R, et al. Training strategies for radiology deep learning models in data-limited scenarios[J]. Radiology: Artificial Intelligence, 2021,3(6):e210 014.
[223] MO R P, ZHOU H, YIN H P, et al.A survey on few-shot learning for remaining useful life prediction[J]. Reliability Engineering & System Safety, 2025,257: 110 850.
[224] HE Z Y, SHAO H D, DING Z Y, et al.Modified deep autoencoder driven by multisource parameters for fault transfer prognosis of aeroengine[J]. IEEE Transactions on Industrial Electronics, 2022,69(1):845-855.
[225] HUANG G J, ZHANG Y L, OU J Y.Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network[J]. Measurement, 2021,176:109 090.
[226] FERREIRA C C M, GONÇALVES G M. Remaining useful life prediction and challenges: A literature review on the use of machine learning methods[J]. Journal of Manufacturing Systems, 2022,63:550-562.
[227] LIU X W, ZHANG Z Z, LI Z L, et al.Advancements in bearing health monitoring and remaining useful life prediction: techniques, challenges, and future directions[J]. Measurement Science and Technology, 2025,36:032 003.
[228] ZHANG H Y, JIANG S L, GAO D F, et al.A review of physics-based, data-driven, and hybrid models for tool wear monitoring[J]. Machines, 2024,12(12):833.
[229] GUO W D, SUN Z C, VILSEN S B, et al.Review of “grey box” lifetime modeling for lithium-ion battery: Combining physics and data-driven methods[J]. Journal of Energy Storage, 2022,56:105 992.
[230] ZHANG L J, LI K W, WANG H, et al.MFC-PINN: A method to improve the accuracy and robustness of acoustic emission source planar localization[J]. Measurement, 2024,235:114 995.
[231] ANTONION K, WANG X, RAISSI M, et al.Machine learning through physics-informed neural networks: Progress and challenges[J]. Academic Journal of Science and Technology, 2024,9(1):46-49.
[232] MENG C Z, GRIESEMER S, CAO D F, et al.When physics meets machine learning: A survey of physics-informed machine learning[J]. Machine Learning for Computational Science and Engineering,2025,1:20.
[233] RAISSI M, PERDIKARIS P, KARNIADAKIS G E.Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019,378:686-707.
[234] MOSELEY B, MARKHAM A, NISSEN-MEYER T.Solving the wave equation with physics-informed deep learning[J]. arXiv preprint arXiv:2006.11894, 2020.
[235] HE G Y, ZHAO Y X, YAN C L.MFLP-PINN: A physics-informed neural network for multiaxial fatigue life prediction[J]. European Journal of Mechanics A: Solids, 2023,98:104 889.
[236] HALAMKA J, BARTOŠÁK M, ŠPANIEL M. Using hybrid physics-informed neural networks to predict lifetime under multiaxial fatigue loading[J]. Engineering Fracture Mechanics, 2023,289:109 351.
[237] ZHANG X C, GONG J G, XUAN F Z.A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures[J]. Engineering Fracture Mechanics, 2021,258:108 130.
[238] DONG Y X, YANG X F, CHANG D D, et al.Predicting fatigue life of multi-defect materials using the fracture mechanics-based physics-informed neural network framework[J]. International Journal of Fatigue, 2025, 190: 108 626.
[239] LI H Q, ZHANG Z X, LI T M, et al.A review on physics-informed data-driven remaining useful life prediction: challenges and opportunities[J]. Mechanical Systems and Signal Processing, 2024, 209: 111 120.
[240] TALREJA R.Manufacturing defects in composites and their effects on performance[M]. Cambridge: Woodhead Publishing, 2014: 99-113.
[241] HUANG S L, WANG S.New technologies in electromagnetic non-destructive testing[M]. Singapore: Springer, 2016.
[242] JIANG W L, LIANG M X, SCHIEBEL M, et al.Development of machine learning based classifier for the pressure test result prediction of type IV composite overwrapped pressure vessels[J]. International Journal of Hydrogen Energy, 2024, 58: 380-388.
[243] MEEMARY B, VASIUKOV D, DELÉGLISE-LAGARDÈRE M, et al. Sensors integration for structural health monitoring in composite pressure vessels: A review[J]. Composite Structures, 2025, 351: 118 546.
[244] LIANG J G, LI C Y, LIU J L, et al.Prediction and optimization of failures in high-pressure hydrogen storage vessels: A review[J]. Renewable and Sustainable Energy Reviews, 2026, 226: 116 236.
[245] SALINAS-CAMUS M, GOEBEL K, ELEFTHEROGLOU N.A comprehensive review and evaluation framework for data-driven prognostics: Uncertainty, robustness, interpretability, and feasibility[J]. Mechanical Systems and Signal Processing, 2025, 237: 113 015.
[246] 陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统, 2021, 27(1): 1-15.
[247] 高士根, 周敏, 郑伟, 等. 基于数字孪生的高端装备智能运维研究现状与展望[J]. 计算机集成制造系统, 2022,28(7):1 953-1 965.
[248] 韩鹏飞, 徐海亮, 程康, 等. 基于数字孪生技术的压力容器安全管理的应用展望[J]. 中国特种设备安全, 2024, 40(S1):103-107.
[249] 郑孟蕾, 田凌. 基于时序数据库的产品数字孪生模型海量动态数据建模方法[J]. 清华大学学报(自然科学版), 2021, 61(11): 1 281-1 288.
[250] 陈三桂, 王泽, 徐峰, 等. 特种压力容器温度数字孪生系统设计与实现[J]. 中国舰船研究,2023,18(5):22-30.
[251] SINGH T, SEHGAL S.Structural health monitoring of composite materials[J]. Archives of Computational Methods in Engineering, 2022,29(4):1 997-2 017.
[252] NI W Z, WANG T, WU Y, et al.Multi-task deep learning model for quantitative volatile organic compounds analysis by feature fusion of electronic nose sensing[J]. Sensors and Actuators B: Chemical, 2024,417:136 206.
[253] WANG Y Q, SONG M M, WANG A, et al. Structural dynamic response reconstruction based on recurrent neural network-aided Kalman filter[J]. Structural Control and Health Monitoring, 2024, 2024(1):7 481 513.
[254] WU T, ZHU W Z, TANG L, et al.Accurate structural displacement reconstruction from acceleration and computer vision measurements using physics-informed neural networks[J]. Mechanical Systems and Signal Processing, 2025,235:112 961.
[255] LUO L X, SUN L M, SONG M M, et al.Joint load-parameter-response identification using a physics-encoded neural network[J]. Mechanical Systems and Signal Processing, 2025, 230:112 597.
[256] DAI T T, JIA Z G, REN L, et al.Kalman filter-based multitype measurement data fusion for stress intensity factor evaluation in marine structures[J]. Structural Control and Health Monitoring, 2023, 2023:2 743 309.
[257] 张诚, 马梓玮, 刘斌, 等. 数字孪生驱动的小样本旋转机械剩余寿命预测[J]. 西安交通大学学报,2023, 57(12):168-178.
[258] 吴泽华, 吴宝英, 赵林杰, 等. 面向输变电设备数字孪生的多物理场正反演快速仿真关键技术综述[J]. 电网技术, 2024,48(10):4 215-4 231.
[259] 陈晓楠, 顾起豪, 靳燕国, 等. 数字孪生仿真预演系统在南水北调中线输水调度中的开发及应用[J].水利信息化,2025(1):14-20.
[260] 肖扬, 李建双, 王华庆, 等. 基于数字孪生技术的复合参量计量研究进展[J]. 计量科学与技术, 2025,69(5): 27-37.
[261] KHALED I, BENNEBACH M, VASIUKOV D, et al.Digital twin for real-time pressure vessels fatigue life prediction[J]. Advances in Mechanical Engineering, 2025,17(5):1-14.
[262] WANG K Y, CHEN Z, ZHANG L, et al.Building a self-evolving digital twin system with Bayesian optimization and deep reinforcement learning for complex equipment optimization and control[J]. Tsinghua Science and Technology, 2026,31(1):199-216.
[263] KHALED I, VASIUKOV D, SHAKOOR M, et al.Digital twin for predicting progressive damage in operating pressure vessels[J]. Procedia Structural Integrity, 2024, 57:280-289.
[264] KANG D, KANG S, KIM B, et al.Condition-based fatigue life monitoring of a high-pressure hydrogen storage vessel using a reduced basis digital twin[J]. Engineering Structures, 2025, 336:120 196.
[265] XU Y J, FEI Y F, HUANG Y L, et al.Advanced corrective training strategy for surrogating complex hysteretic behavior[J]. Structures,2022,41:1 792-1 803.
[266] 轩福贞, 朱明亮, 王国彪. 结构疲劳百年研究的回顾与展望[J]. 机械工程学报, 2021,57(6):26-51.
[267] WANG H J, LI B, GONG J G, et al.Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods[J]. Engineering Fracture Mechanics, 2023, 284: 109 242.
[268] 孙兴悦, 刘耘宇, 马玉娥,等. 基于数据-物理融合驱动方法的Ti6Al4V多轴疲劳寿命预测研究[J]. 固体力学学报,2025,46(5):571-588.
[269] WANG H J, LI B, LEI L M, et al. Multi-physics information-integrated neural network for fatigue life prediction of additively manufactured Hastelloy X superalloy[J]. Virtual and Physical Prototyping, 2024, 19(1): 2 368 652.
[270] WANG L Y, ZHU S P, WU B R, et al.Multi-fidelity physics-informed machine learning framework for fatigue life prediction of additive manufactured materials[J]. Computer Methods in Applied Mechanics and Engineering, 2025,439:117 924.

基金

国家重点研发计划资助项目(2024YFF0505200); 自然科学基金资助项目(12222206); 上海集成电路领域基础研究计划项目(25JD1403700)

Accesses

Citation

Detail

段落导航
相关文章

/