2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

动态系统的实时安全性评估技术

何潇 刘泽夷 胡嵩乔 刘畅 周东华

何潇, 刘泽夷, 胡嵩乔, 刘畅, 周东华. 动态系统的实时安全性评估技术. 自动化学报, 2025, 51(2): 1−22 doi: 10.16383/j.aas.c240096
引用本文: 何潇, 刘泽夷, 胡嵩乔, 刘畅, 周东华. 动态系统的实时安全性评估技术. 自动化学报, 2025, 51(2): 1−22 doi: 10.16383/j.aas.c240096
He Xiao, Liu Ze-Yi, Hu Song-Qiao, Liu Chang, Zhou Dong-Hua. Real-time safety assessment techniques of dynamic systems. Acta Automatica Sinica, 2025, 51(2): 1−22 doi: 10.16383/j.aas.c240096
Citation: He Xiao, Liu Ze-Yi, Hu Song-Qiao, Liu Chang, Zhou Dong-Hua. Real-time safety assessment techniques of dynamic systems. Acta Automatica Sinica, 2025, 51(2): 1−22 doi: 10.16383/j.aas.c240096

动态系统的实时安全性评估技术

doi: 10.16383/j.aas.c240096 cstr: 32138.14.j.aas.c240096
基金项目: 国家重点研发计划(2022YFB25031103), 国家自然科学基金(61733009), 华能集团科技研究项目(HNKJ22-H105)资助
详细信息
    作者简介:

    何潇:清华大学自动化系长聘教授. 2010年获得清华大学博士学位. 主要研究方向为动态系统的故障诊断与容错控制. 本文通信作者. E-mail: hexiao@tsinghua.edu.cn

    刘泽夷:清华大学自动化系博士研究生. 主要研究方向为动态系统的故障诊断与安全性评估. E-mail: liuzy21@mails.tsinghua.edu.cn

    胡嵩乔:清华大学自动化系博士研究生. 主要研究方向为动态系统的故障诊断与安全性评估. E-mail: hsq23@mails.tsinghua.edu.cn

    刘畅:清华大学自动化系博士研究生. 主要研究方向为动态系统的故障诊断与安全性评估. E-mail: liuc19@mails.tsinghua.edu.cn

    周东华:山东科技大学和清华大学教授. 主要研究方向为动态系统的故障诊断与容错控制, 故障预测与最优维护技术. E-mail: zdh@mail.tsinghua.edu.cn

Real-time Safety Assessment Techniques of Dynamic Systems

Funds: Supported by National Key Research and Development Program of China (2022YFB25031103), National Natural Science Foundation of China (61733009), and Huaneng Group Science and Technology Research Project (HNKJ22-H105)
More Information
    Author Bio:

    HE Xiao Tenured professor in the Department of Automation, Tsinghua University. He received his Ph.D. degree from Tsinghua University in 2010. His research interest covers fault diagnosis and fault tolerant control for dynamic systems. Corresponding author of this paper

    LIU Ze-Yi Ph.D. candidate in the Department of Automation, Tsinghua University. His research interest covers fault diagnosis and safety assessment for dynamic systems

    HU Song-Qiao Ph.D. candidate in the Department of Automation, Tsinghua University. His research interst covers fault diagnosis and safety assessment for dynamic systems

    LIU Chang Ph.D. candidate in the Department of Automation, Tsinghua University. His research interst covers fault diagnosis and safety assessment for dynamic systems

    ZHOU Dong-Hua Professor at Shandong University of Science and Technology and Tsinghua University. His research interest covers fault diagnosis and tolerant control, fault prediction, and optimal maintenance

  • 摘要: 动态系统的实时安全性评估(Real-time safety assessment, RTSA)在防止潜在安全事故导致重大损失方面发挥着关键作用. 随着系统功能和操作环境复杂性的日益增加, 开发有效的实时安全性评估技术面临着更大的挑战. 鉴于此, 阐述了动态系统实时安全性评估的概念定义, 从环境的平稳性及评估模型的构建方式两个维度出发提出了一种分类框架, 给出了相应的问题描述, 较系统地回顾了动态系统实时安全性评估技术的现有进展, 讨论了针对不同实际系统的部署策略, 分析了现有技术的发展趋势, 探讨了实时安全性评估中亟待解决的问题与未来的发展方向.
    1)  11 在Electropedia中, 风险性定义为危害发生的概率与该危害的严重性之间的结合关系[39], 该定义更加侧重于描述危害发生的可能性与危害的严重性之间的共同作用. 在系统工程领域, 风险分析通常被建模为危害发生的概率与危害严重性的量化之间的函数关系[40]. 对于一些非实时的应用场景, 风险性可以被视为与安全性相似的概念[41-43]. 本文侧重于讨论动态系统的实时安全性评估技术.
    2)  22 在一些文献中也称作障碍验证 (Barrier certificates).
    3)  33 在一些文献中也称之为系统健康状态 (Health state).
    4)  44 该类技术在一些文献中又称之为基于状态监测的安全性评估 (Condition monitoring-based safety assessment, CMBSA) 或基于状态监测的风险评估 (Condition monitoring-based risk assessment, CMBRA)[14].
    5)  55 依据第1.3节所描述的定义, 使用风险模型进行评估但未使用状态监测数据的方法, 不属于本文所重点讨论的范畴.
    6)  66 自20世纪中后期以来, 道路安全评估方法 (Road safety assessment) 取得了一定的成功[215216], 由于其在整体上依赖于对事故数据的分析, 而非基于系统测量进行分析, 因此不在本文中重点讨论.
    7)  77 参见引言, 其与动态安全性评估 (Dynamic safety assessment, DSA) 技术存在本质区别.
  • 图  1  不同学术概念对应的示意场景: 以无人机飞行过程为例

    Fig.  1  Illustrative scenarios corresponding to different academic concepts: A case study of UAV flight processes

    图  2  动态系统实时故障诊断与实时安全性评估示意图

    Fig.  2  Schematic diagram of real-time fault diagnosis and real-time safety assessment for dynamic systems

    图  3  动态系统的实时安全性评估方法分类示意图

    Fig.  3  Schematic diagram of the taxonomy of real-time safety assessment approaches for dynamic systems

    图  4  不同安全威胁对应的安全性等级示意图: 以深潜器为例

    Fig.  4  Schematic diagram of safety levels corresponding to different safety threats: a case study of a DSMS

    图  5  分类讨论逻辑结构示意图

    Fig.  5  Schematic diagram of the logical structure for categorized discussion

    表  1  几类相关概念在国家军用系列标准下的描述及主要侧重角度

    Table  1  Descriptions and key focuses of several related concepts under national military standards

    概念 描述 主要侧重 参考来源
    安全性 不导致人员伤亡、装备损坏、财产损失或不危及人员健康和环境的能力 系统状态、环境状态 文献[4]
    可靠性 在特定时间段内, 系统不发生故障且能够按要求执行任务的能力 系统状态 文献[7]
    测试性 及时准确地确定系统状态, 并隔离其内部故障的设计特性 系统状态 文献[8]
    下载: 导出CSV

    表  2  经典安全性评估方法与实时安全性评估方法的概念辨析

    Table  2  Conceptual analysis of classical safety assessment approaches and real-time safety assessment approaches

    经典安全性评估 实时安全性评估
    先验知识需求 较多 较少
    计算资源需求 较少 较多
    主体适用对象 系统级 部件级
    主要侧重阶段 方案设计阶段 使用保障阶段
    典型应用领域 核能、航空航天 电力、工程结构
    现有理论成果 较多 较少
    应用价值 较高 较高
    下载: 导出CSV

    表  3  不同环境条件下实时安全性评估方法的研究情况

    Table  3  Research of real-time safety assessment approaches under different environmental conditions

    所依据的主要理论体系 平稳环境 非平稳环境
    状态空间 极多 极少
    风险模型 较少 较多
    专家系统 较多 较少
    统计学习 较多 较多
    深度学习 极多 极少
    信号处理
    下载: 导出CSV

    表  4  不同环境条件下实时安全性评估方法的研究特点

    Table  4  Features of real-time safety assessment approaches under different environmental conditions

    平稳环境 非平稳环境
    是否利用系统输入输出
    系统特性变化 较少 较多
    更新能力需求
    外部因素影响程度
    问题困难程度 较低 较高
    实际应用范围 较小 较大
    评估模型稳定性 较高 较低
    决策反馈要求
    现有理论成果 较多 较少
    下载: 导出CSV

    表  5  几类典型动态系统在实时安全性评估框架下的现有进展

    Table  5  Current advances in real-time safety assessment frameworks for several typical dynamic systems

    系统对象 参考文献
    平稳环境下显式分析方法 平稳环境下隐式分析法 非平稳环境下显式分析法 非平稳环境下隐式分析法
    工程结构系统 [154158] [74, 8485, 9495, 107108, 159163] [126, 129, 164166] [167168]
    交通系统 [21, 169] [170172] [173174] [150153, 175]
    电力系统 [176] [8082, 86, 88, 9193, 9697, 177180] [135136, 140] [143149, 181]
    核能发电系统 [19, 182183] [184] [34, 36, 127, 132, 137, 139, 185] [186]
    化工系统 [187188] [189191] [44, 130131] [192]
    航空航天系统 [79, 193195] [196] [37, 128, 197] [198]
    下载: 导出CSV
  • [1] Brin M, Stuck G. Introduction to Dynamical Systems, Cambridge: Cambridge University Press, 2002.
    [2] 周东华, 胡艳艳. 动态系统的故障诊断技术. 自动化学报, 2009, 35(6): 748−758 doi: 10.3724/SP.J.1004.2009.00748

    Zhou Dong-Hua, Hu Yan-Yan. Fault diagnosis techniques for dynamic systems. Acta Automatica Sinica, 2009, 35(6): 748−758 doi: 10.3724/SP.J.1004.2009.00748
    [3] Smith R E. Quantitative vs. Qualitative ESOH Risk Assessments Using the 882E Risk Matrix, MIL-STD-882E, San Diego, USA, 2012.

    Smith R E. Quantitative vs. Qualitative ESOH Risk Assessments Using the 882E Risk Matrix, MIL-STD-882E, San Diego, USA, 2012.
    [4] 中国人民解放军总装备部. 装备安全性工作通用要求, GJB 900A-2012, 2012.

    General Equipment Department of the Chinese People' s Liberation Army. General Requirements for Materiel Safety Program, GJB 900A-2012, 2012.
    [5] International Electrotechnical Commission. International Electrotechnical Vocabulary Online Database (351-57-05. Safety). 2022.

    International Electrotechnical Commission. International Electrotechnical Vocabulary Online Database (351-57-05. Safety). 2022.
    [6] Isermann R, Ballé P. Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 1997, 5(5): 709−719 doi: 10.1016/S0967-0661(97)00053-1
    [7] 中央军委装备发展部. 装备可靠性工作通用要求, GJB 450B-2021, 2021.

    Equipment Development Department of People's Republic of China Central Military Commission. General Requirements for Equipment Reliability Work, GJB 450B-2021, 2021.
    [8] 中国人民解放军总装备部. 装备测试性工作通用要求, GJB 2547A-2012, 2012.

    General Equipment Department of the Chinese People's Liberation Army. General Requirements for Equipment Testing Work, GJB 2547A-2012, 2012.
    [9] Aldemir T. A survey of dynamic methodologies for probabilistic safety assessment of nuclear power plants. Annals of Nuclear Energy, 2013, 52: 113−124 doi: 10.1016/j.anucene.2012.08.001
    [10] Li B Q, Wen S P, Yan Z, Wen G H, Huang T W. A survey on the control lyapunov function and control barrier function for nonlinear-affine control systems. IEEE/CAA Journal of Automatica Sinica, 2023, 10(3): 584−602 doi: 10.1109/JAS.2023.123075
    [11] Liu Z Y, Hu S Q, He X. Real-time safety assessment of dynamic systems in non-stationary environments: A review of methods and techniques. In: Proceedings of the CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). Yibin, China: IEEE, 2023. 1−6
    [12] 柴毅, 毛万标, 任浩, 屈剑锋, 尹宏鹏, 杨志敏, 等. 航天发射系统运行安全性评估研究进展与挑战. 自动化学报, 2019, 45(10): 1829−1845

    Chai Yi, Mao Wan-Biao, Ren Hao, Qu Jian-Feng, Yin Hong-Peng, Yang Zhi-Min, et al. Research on operational safety assessment for spacecraft launch system: Progress and challenges. Acta Automatica Sinica, 2019, 45(10): 1829−1845
    [13] Liu C, He X, Zhou D H, Huang B. Safety assessment for dynamic systems: A survey. Cybernetics and Intelligence, DOI: 10.26599/CAI.2024.9390001
    [14] Zio E. The future of risk assessment. Reliability Engineering and System Safety, 2018, 177: 176−190
    [15] Stamatis D H. Failure Mode and Effect Analysis. Quality Press, 2003.

    Stamatis D H. Failure Mode and Effect Analysis. Quality Press, 2003.
    [16] Liu H C, Liu L, Liu N. Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems With Applications, 2013, 40(2): 828−838 doi: 10.1016/j.eswa.2012.08.010
    [17] Zio E. Integrated deterministic and probabilistic safety assessment: Concepts, challenges, research directions. Nuclear Engineering and Design, 2014, 280: 413−419 doi: 10.1016/j.nucengdes.2014.09.004
    [18] de Vasconcelos V, Soares W A, da Costa A C L, Raso A L. Deterministic and probabilistic safety analyses. Advances in System Reliability Engineering. London: Academic Press, 2019. 43−75
    [19] Holmberg J E, Kahlbom U. Application of human reliability analysis in the deterministic safety analysis for nuclear power plants. Reliability Engineering and System Safety, 2020, 194: Article No. 106371
    [20] Rausand M. Preliminary Hazard Analysis, Norwegian University of Science and Technology, Norwegian, 2005.

    Rausand M. Preliminary Hazard Analysis, Norwegian University of Science and Technology, Norwegian, 2005.
    [21] Hadj-Mabrouk H. Preliminary Hazard Analysis (PHA): New hybrid approach to railway risk analysis. International Refereed Journal of Engineering and Science, 2017, 6(2): 51−58
    [22] Lee W S, Grosh D L, Tillman F A, Lie C H. Fault tree analysis, methods, and applications-a review. IEEE Transactions on Reliability, 1985, R-34(3): 194−203

    Lee W S, Grosh D L, Tillman F A, Lie C H. Fault tree analysis, methods, and applications-a review. IEEE Transactions on Reliability, 1985, R-34 (3): 194−203
    [23] Xing L D, Amari S V. Fault tree analysis. Handbook of Performability Engineering. London: Springer, 2008. 595−620
    [24] Andrews J D, Dunnett S J. Event-tree analysis using binary decision diagrams. IEEE Transactions on Reliability, 2000, 49(2): 230−238 doi: 10.1109/24.877343
    [25] Ferdous R, Khan F, Sadiq R, Amyotte P, Veitch B. Handling data uncertainties in event tree analysis. Process Safety and Environmental Protection, 2009, 87(5): 283−292 doi: 10.1016/j.psep.2009.07.003
    [26] Dunjó J, Fthenakis V, Vílchez J A, Arnaldos J. Hazard and operability (HAZOP) analysis. A literature review. Journal of Hazardous Materials, 2010, 173(1−3): 19−32 doi: 10.1016/j.jhazmat.2009.08.076
    [27] Baybutt P. A critique of the Hazard and Operability (HAZOP) study. Journal of Loss Prevention in the Process Industries, 2015, 33: 52−58 doi: 10.1016/j.jlp.2014.11.010
    [28] Stranks J. Human Factors and Behavioural Safety. Oxford: Butterworth-Heinemann, 2007.
    [29] Booth R T, Lee T R. The role of human factors and safety culture in safety management. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 1995, 209(5): 393−400 doi: 10.1243/PIME_PROC_1995_209_098_02
    [30] Aldemir T, Siu N O, Mosleh A, Cacciabue P C, Göktepe B G. Reliability and Safety Assessment of Dynamic Process Systems. Berlin: Springer, 2013. 120
    [31] Liu Z Y, Xiao F Y. An intuitionistic evidential method for weight determination in FMEA based on belief entropy. Entropy, 2019, 21(2): Article No. 211 doi: 10.3390/e21020211
    [32] 周家红, 许开立, 陈志勇. 系统动态安全评价研究. 东北大学学报(自然科学版), 2008, 29(3): 416−419 doi: 10.3321/j.issn:1005-3026.2008.03.029

    Zhou Jia-Hong, Xu Kai-Li, Chen Zhi-Yong. On the dynamic assessment of system safety. Journal of Northeastern University (Natural Science), 2008, 29(3): 416−419 doi: 10.3321/j.issn:1005-3026.2008.03.029
    [33] Holmberg J, Niemelae I. Risk Measures in Living Probabilistic Safety Assessment, VTT-PUB--146, Technical Research Centre of Finland, Finland, 1993.
    [34] Kančev D, Cepin M, Gjorgiev B. Development and application of a living probabilistic safety assessment tool: Multi-objective multi-dimensional optimization of surveillance requirements in NPPs considering their ageing. Reliability Engineering and System Safety, 2014, 131: 135−147
    [35] Cepin M. The extended living probabilistic safety assessment. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2020, 234(1): 183−192

    Cepin M. The extended living probabilistic safety assessment. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2020, 234(1): 183−192
    [36] Yang J, Yang M, Wang W L, Li F J. Online application of a risk management system for risk assessment and monitoring at NPPs. Nuclear Engineering and Design, 2016, 305: 200−212 doi: 10.1016/j.nucengdes.2016.05.025
    [37] Zarei E, Azadeh A, Khakzad N, Aliabadi M M, Mohammadfam I. Dynamic safety assessment of natural gas stations using Bayesian network. Journal of Hazardous Materials, 2017, 321: 830−840 doi: 10.1016/j.jhazmat.2016.09.074
    [38] Podofillini L, Zio E, Mercurio D, Dang V N. Dynamic safety assessment: Scenario identification via a possibilistic clustering approach. Reliability Engineering and System Safety, 2010, 95(5): 534−549
    [39] International Electrotechnical Commission. International Electrotechnical Vocabulary Online Database (351-57-03, Risk). 2022.

    International Electrotechnical Commission. International Electrotechnical Vocabulary Online Database (351-57-03, Risk). 2022.
    [40] Vališ D. Contribution to reliability and safety assessment of systems. Safety and Reliability, 2007, 27(3): 23−35 doi: 10.1080/09617353.2007.11690840
    [41] Siu N. Risk assessment for dynamic systems: An overview. Reliability Engineering and System Safety, 1994, 43(1): 43−73
    [42] Moradi R, Groth K M. Modernizing risk assessment: A systematic integration of PRA and PHM techniques. Reliability Engineering and System Safety, 2020, 204: 107194
    [43] Hollnagel E. Safety-I and Safety-II: The Past and Future of Safety Management. CRC Press, 2018.

    Hollnagel E. Safety-I and Safety-II: The Past and Future of Safety Management. CRC Press, 2018.
    [44] Villa V, Paltrinieri N, Khan F, Cozzani V. Towards dynamic risk analysis: A review of the risk assessment approach and its limitations in the chemical process industry. Safety Science, 2016, 89: 77−93 doi: 10.1016/j.ssci.2016.06.002
    [45] 何潇, 郭亚琦, 张召, 贾繁林, 周东华. 动态系统的主动故障诊断技术. 自动化学报, 2020, 46(8): 1557−1570

    He Xiao, Guo Ya-Qi, Zhang Zhao, Jia Fan-Lin, Zhou Dong-Hua. Active fault diagnosis for dynamic systems. Acta Automatica Sinica, 2020, 46(8): 1557−1570
    [46] Hu S Q, Liu Z Y, Li M Y, He X. CADM +: Confusion-based learning framework with drift detection and adaptation for real-time safety assessment. IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2024.3369315
    [47] Ditzler G, Roveri M, Alippi C, Polikar R. Learning in nonstationary environments: A survey. IEEE Computational Intelligence Magazine, 2015, 10(4): 12−25 doi: 10.1109/MCI.2015.2471196
    [48] Ahmadi M, Israel A, Topcu U. Safety assessemt based on physically-viable data-driven models. In: Proceedings of the IEEE 56th Annual Conference on Decision and Control (CDC). Melbourne, VIC, Australia: IEEE, 2017. 6409−6414
    [49] Knight J C. Safety critical systems: Challenges and directions. In: Proceedings of the 24th International Conference on Software Engineering. Orlando, FL, USA: IEEE, 2002. 547−550
    [50] Rausand M. Reliability of Safety-critical Systems: Theory and Applications. Hoboken: Wiley Publishing, 2014.
    [51] Ames A D, Xu X R, Grizzle J W, Tabuada P. Control barrier function based quadratic programs for safety critical systems. IEEE Transactions on Automatic Control, 2017, 62(8): 3861−3876 doi: 10.1109/TAC.2016.2638961
    [52] Clarke E M. Model checking. In: Proceedings of the 17th Conference on Foundations of Software Technology and Theoretical Computer Science. Kharagpur, India: Springer, 1997. 54−56
    [53] Alur R, Dang T, Ivančić F. Progress on reachability analysis of hybrid systems using predicate abstraction. In: Proceedings of the 6th International Workshop on Hybrid Systems: Computation and Control. Prague, Czech Republic: Springer, 2003. 4−19
    [54] Prajna S, Jadbabaie A, Pappas G J. Stochastic safety verification using barrier certificates. In: Proceedings of the 43rd IEEE Conference on Decision and Control (CDC). Nassau, Bahamas: IEEE, 2004. 929−934
    [55] Prajna S, Rantzer A. On the necessity of barrier certificates. IFAC Proceedings Volumes, 2005, 38(1): 526−531
    [56] Wang G B, Liu J, Sun H Y, Liu J, Ding Z H, Zhang M M. Safety verification of state/time-driven hybrid systems using barrier certificates. In: Proceedings of the 35th Chinese Control Conference (CCC). Chengdu, China: IEEE, 2016. 2483−2489
    [57] Ames A D, Coogan S, Egerstedt M, Notomista G, Sreenath K, Tabuada P. Control barrier functions: Theory and applications. In: Proceedings of the 18th European Control Conference (ECC). Naples, Italy: IEEE, 2019. 3420−3431
    [58] Xiao W, Cassandras C G, Belta C. Safe Autonomy with Control Barrier Functions: Theory and Applications. Cham: Springer, 2023.
    [59] Nguyen Q, Sreenath K. Exponential control barrier functions for enforcing high relative-degree safety-critical constraints. In: Proceedings of the American Control Conference (ACC). Boston, MA, USA: IEEE, 2016. 322−328
    [60] Xiao W, Belta C. Control barrier functions for systems with high relative degree. In: Proceedings of the IEEE 58th Conference on Decision and Control (CDC). Nice, France: IEEE, 2019. 474−479
    [61] Romdlony M Z, Jayawardhana B. Stabilization with guaranteed safety using control Lyapunov-barrier function. Automatica, 2016, 66: 39−47 doi: 10.1016/j.automatica.2015.12.011
    [62] Xu X R, Tabuada P, Grizzle J W, Ames A D. Robustness of control barrier functions for safety critical control. IFAC-PapersOnLine, 2015, 48(27): 54−61 doi: 10.1016/j.ifacol.2015.11.152
    [63] Zhu Z R, Chai Y, Yang Z M. A novel kind of sufficient conditions for safety judgement based on control barrier function. Science China Information Sciences, 2021, 64(9): Article No. 199205 doi: 10.1007/s11432-018-9840-6
    [64] Zhu Z R, Chai Y, Yang Z M, Huang C H. Exponential-alpha safety criteria of a class of dynamic systems with barrier functions. IEEE/CAA Journal of Automatica Sinica, 2022, 9(11): 1939−1951 doi: 10.1109/JAS.2020.1003408
    [65] Liu S M, Liu C L, Dolan J. Safe control under input limits with neural control barrier functions. In: Proceedings of the 6th Conference on Robot Learning. Auckland, New Zealand: PMLR, 2023. 1970−1980
    [66] Zhang Z Y, Zhao Q C, Sun K L. A learning-based method for computing control barrier functions of nonlinear systems with control constraints. IEEE Robotics and Automation Letters, 2023, 8(7): 4259−4266 doi: 10.1109/LRA.2023.3281930
    [67] Liu S H, Liu L J, Yu Z. Safe reinforcement learning for affine nonlinear systems with state constraints and input saturation using control barrier functions. Neurocomputing, 2023, 518: 562−576 doi: 10.1016/j.neucom.2022.11.006
    [68] Bujorianu M L, Wisniewski R, Boulougouris E. p-safety and stability. IFAC-PapersOnLine, 2021, 54(9): 665−670 doi: 10.1016/j.ifacol.2021.06.127
    [69] Wisniewski R, Bujorianu L M. Safety of stochastic systems: An analytic and computational approach. Automatica, 2021, 133: Article No. 109839 doi: 10.1016/j.automatica.2021.109839
    [70] Wisniewski R, Bujorianu M L, Sloth C. p-safe analysis of stochastic hybrid processes. IEEE Transactions on Automatic Control, 2020, 65(12): 5220−5235 doi: 10.1109/TAC.2020.2972789
    [71] Girard A. Controller synthesis for safety and reachability via approximate bisimulation. Automatica, 2012, 48(5): 947−953 doi: 10.1016/j.automatica.2012.02.037
    [72] Xiang W M, Tran H D, Johnson T T. Output reachable set estimation for switched linear systems and its application in safety verification. IEEE Transactions on Automatic Control, 2017, 62(10): 5380−5387 doi: 10.1109/TAC.2017.2692100
    [73] Schürmann B, Klischat M, Kochdumper N, Althoff M. Formal safety net control using backward reachability analysis. IEEE Transactions on Automatic Control, 2022, 67(11): 5698−5713 doi: 10.1109/TAC.2021.3124188
    [74] 张燕, 周围, 丛培江. 基于模糊规则推理的大坝安全监测变形预测模型. 水电自动化与大坝监测, 2009, 33(2): 51−54

    Zhang Yan, Zhou Wei, Cong Pei-Jiang. Fuzzy inference-based deformation prediction model for dam safety monitoring. Hydropower Automation and Dam Monitoring, 2009, 33(2): 51−54
    [75] Li G L, Zhou Z J, Hu C H, Chang L L, Zhou Z G, Zhao F J. A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base. Safety Science, 2017, 93: 108−120 doi: 10.1016/j.ssci.2016.11.011
    [76] Li G L, Zhou Z J, Hu C H, Chang L L, Zhang H T, Yu C Q. An optimal safety assessment model for complex systems considering correlation and redundancy. International Journal of Approximate Reasoning, 2019, 104: 38−56 doi: 10.1016/j.ijar.2018.10.004
    [77] Tang S W, Zhou Z J, Hu C H, Zhao F J, Cao Y. A new evidential reasoning rule-based safety assessment method with sensor reliability for complex systems. IEEE Transactions on Cybernetics, 2022, 52(5): 4027−4038 doi: 10.1109/TCYB.2020.3015664
    [78] Liu Z Y, Deng Y, Zhang Y, Ding Z J, He X. Safety assessment of dynamic systems: An evidential group interaction-based fusion design. IEEE Transactions on Instrumentation and Measurement, 2021, 70: Article No. 3523014
    [79] Zhou Z J, Feng Z C, Hu C H, Hu G Y, He W, Han X X. Aeronautical relay health state assessment model based on belief rule base with attribute reliability. Knowledge-Based Systems, 2020, 197: Article No. 105869 doi: 10.1016/j.knosys.2020.105869
    [80] Tomin N V, Kurbatsky V G, Sidorov D N, Zhukov A V. Machine learning techniques for power system security assessment. IFAC-PapersOnLine, 2016, 49(27): 445−450 doi: 10.1016/j.ifacol.2016.10.773
    [81] Wehenkel L, Pavella M. Decision tree approach to power systems security assessment. International Journal of Electrical Power and Energy Systems, 1993, 15(1): 13−36
    [82] Krishnan V, McCalley J D, Henry S, Issad S. Efficient database generation for decision tree based power system security assessment. IEEE Transactions on Power Systems, 2011, 26(4): 2319−2327 doi: 10.1109/TPWRS.2011.2112784
    [83] Hatziargyriou N D, Contaxis G C, Sideris N C. A decision tree method for on-line steady state security assessment. IEEE Transactions on Power Systems, 1994, 9(2): 1052−1061 doi: 10.1109/59.317626
    [84] Nazarko P, Ziemiański L. Application of artificial neural networks in the damage identification of structural elements. Computer Assisted Methods in Engineering and Science, 2017, 18(3): 175−189
    [85] Yu J B. A hybrid feature selection scheme and self-organizing map model for machine health assessment. Applied Soft Computing, 2011, 11(5): 4041−4054 doi: 10.1016/j.asoc.2011.03.026
    [86] Bellizio F, Cremer J L, Sun M Y, Strbac G. A causality based feature selection approach for data-driven dynamic security assessment. Electric Power Systems Research, 2021, 201: Article No. 107537 doi: 10.1016/j.jpgr.2021.107537
    [87] Liu C X, Tang F, Leth Bak C. An accurate online dynamic security assessment scheme based on random forest. Energies, 2018, 11(7): Article No. 1914 doi: 10.3390/en11071914
    [88] Liu S K, Liu L H, Yang N, Mao D, Zhang L, Cheng J Z, et al. A data-driven approach for online dynamic security assessment with spatial-temporal dynamic visualization using random bits forest. International Journal of Electrical Power and Energy Systems, 2021, 124: Article No. 106316
    [89] Liu S K, Liu L H, Fan Y P, Zhang L, Huang Y H, Zhang T, et al. An integrated scheme for online dynamic security assessment based on partial mutual information and iterated random forest. IEEE Transactions on Smart Grid, 2020, 11(4): 3606−3619 doi: 10.1109/TSG.2020.2991335
    [90] He M, Zhang J S, Vittal V. A data mining framework for online dynamic security assessment: Decision trees, boosting, and complexity analysis. In: Proceedings of the IEEE PES Innovative Smart Grid Technologies (ISGT). Washington, DC, USA: IEEE, 2012. 1−8
    [91] Xu Y, Dong Z Y, Zhao J H, Zhang P, Wong K P. A reliable intelligent system for real-time dynamic security assessment of power systems. IEEE Transactions on Power Systems, 2012, 27(3): 1253−1263 doi: 10.1109/TPWRS.2012.2183899
    [92] Liu R D, Verbič G, Xu Y. A new reliability-driven intelligent system for power system dynamic security assessment. In: Proceedings of the Australasian Universities Power Engineering Conference (AUPEC). Melbourne, VIC, Australia: IEEE, 2017. 1−6
    [93] Rizwan-ul-Hassan, Li C G, Liu Y T. Online dynamic security assessment of wind integrated power system using SDAE with SVM ensemble boosting learner. International Journal of Electrical Power and Energy Systems, 2021, 125: Article No. 106429
    [94] Sarmadi H, Entezami A, Saeedi Razavi B, Yuen K V. Ensemble learning-based structural health monitoring by Mahalanobis distance metrics. Structural Control and Health Monitoring, 2021, 28(2): Article No. e2663
    [95] Dworakowski Z, Stepinski T, Dragan K, Jablonski A, Barszcz T. Ensemble ANN classifier for structural health monitoring. In: Proceedings of the 15th International Conference on Artificial Intelligence and Soft Computing. Zakopane, Poland: Springer, 2016. 81−90
    [96] Liu T J, Liu Y B, Liu J Y, Wang L F, Xu L X, Qiu G, et al. A Bayesian learning based scheme for online dynamic security assessment and preventive control. IEEE Transactions on Power Systems, 2020, 35(5): 4088−4099 doi: 10.1109/TPWRS.2020.2983477
    [97] He M, Vittal V, Zhang J S. Online dynamic security assessment with missing PMU measurements: A data mining approach. IEEE Transactions on Power Systems, 2013, 28(2): 1969−1977 doi: 10.1109/TPWRS.2013.2246822
    [98] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436−444 doi: 10.1038/nature14539
    [99] Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018, 2018(1): Article No. 7068349
    [100] Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D. Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(7): 3523−3542
    [101] Ayodeji A, Amidu M A, Olatubosun S A, Addad Y, Ahmed H. Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities. Progress in Nuclear Energy, 2022, 151: Article No. 104339 doi: 10.1016/j.pnucene.2022.104339
    [102] Ye X W, Jin T, Yun C B. A review on deep learning-based structural health monitoring of civil infrastructures. Smart Structures and Systems, 2019, 24(5): 567−585
    [103] Liu C, Zhang Y, He X. Expert-augmented data-driven safety level assessment scheme with incremental learning. In: Proceedings of the CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS). Chengdu, China: IEEE, 2021. 1−6
    [104] Li Z W, Liu F, Yang W J, Peng S H, Zhou J. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(12): 6999−7019 doi: 10.1109/TNNLS.2021.3084827
    [105] Alawad H, Kaewunruen S, An M. A deep learning approach towards railway safety risk assessment. IEEE Access, 2020, 8: 102811−102832 doi: 10.1109/ACCESS.2020.2997946
    [106] Sun M Y, Konstantelos I, Strbac G. A deep learning-based feature extraction framework for system security assessment. IEEE Transactions on Smart Grid, 2019, 10(5): 5007−5020 doi: 10.1109/TSG.2018.2873001
    [107] Sarkar S, Reddy K K, Giering M, Gurvich M R. Deep learning for structural health monitoring: A damage characterization application. Annual Conference of the PHM Society, 2016, 8(1): 1−7
    [108] Azimi M, Pekcan G. Structural health monitoring using extremely compressed data through deep learning. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(6): 597−614 doi: 10.1111/mice.12517
    [109] Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath A A. Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 2018, 35(1): 53−65 doi: 10.1109/MSP.2017.2765202
    [110] Ren C, Xu Y. A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data. IEEE Transactions on Power Systems, 2019, 34(6): 5044−5052 doi: 10.1109/TPWRS.2019.2922671
    [111] Warnecke A, Arp D, Wressnegger C, Rieck K. Evaluating explanation methods for deep learning in security. In: Proceedings of the IEEE European Symposium on Security and Privacy (EuroS&P). Genoa, Italy: IEEE, 2020. 158−174
    [112] Guo W B, Mu D L, Xu J, Su P R, Wang G, Xing X Y. LEMNA: Explaining deep learning based security applications. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. Toronto, Canada: ACM, 2018. 367−379
    [113] Liu C, Zhang Y, Ding Z J, He X. Active incremental learning for health state assessment of dynamic systems with unknown scenarios. IEEE Transactions on Industrial Informatics, 2023, 19(2): 1863−1873 doi: 10.1109/TII.2022.3181187
    [114] Liu C, He X, Li M Y, Zhang Y, Ding Z J. Active labeling aided semi-supervised safety assessment with task-related unknown scenarios. IEEE Transactions on Reliability, 2024, 73 (4): 1792−1804
    [115] Xiao W, Cassandras C G, Belta C. Adaptive control barrier functions. Safe Autonomy with Control Barrier Functions: Theory and Applications. Cham: Springer, 2023. 73−94
    [116] Dhiman V, Khojasteh M J, Franceschetti M, Atanasov N. Control barriers in Bayesian learning of system dynamics. IEEE Transactions on Automatic Control, 2023, 68(1): 214−229 doi: 10.1109/TAC.2021.3137059
    [117] Taylor A J, Ames A D. Adaptive safety with control barrier functions. In: Proceedings of the American Control Conference (ACC). Denver, CO, USA: IEEE, 2020. 1399−1405
    [118] Lopez B T, Slotine J J E, How J P. Robust adaptive control barrier functions: An adaptive and data-driven approach to safety. IEEE Control Systems Letters, 2021, 5(3): 1031−1036 doi: 10.1109/LCSYS.2020.3005923
    [119] Xiao W, Belta C, Cassandras C G. Adaptive control barrier functions. IEEE Transactions on Automatic Control, 2022, 67(5): 2267−2281 doi: 10.1109/TAC.2021.3074895
    [120] Xiao W, Wang T H, Hasani R, Chahine M, Amini A, Li X, et al. Barriernet: Differentiable control barrier functions for learning of safe robot control. IEEE Transactions on Robotics, 2023, 39(3): 2289−2307 doi: 10.1109/TRO.2023.3249564
    [121] Hu J Q, Zhang L B, Liang W. An adaptive online safety assessment method for mechanical system with pre-warning function. Safety Science, 2012, 50(3): 385−399 doi: 10.1016/j.ssci.2011.09.018
    [122] 赵福均, 周志杰, 胡昌华, 常雷雷, 王力. 基于证据推理的动态系统安全性在线评估方法. 自动化学报, 2017, 43(11): 1950−1961

    Zhao Fu-Jun, Zhou Zhi-Jie, Hu Chang-Hua, Chang Lei-Lei, Wang Li. Online safety assessment method based on evidential reasoning for dynamic systems. Acta Automatica Sinica, 2017, 43(11): 1950−1961
    [123] Zhao F J, Zhou Z J, Hu C H, Chang L L, Zhou Z G, Li G L. A new evidential reasoning-based method for online safety assessment of complex systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(6): 954−966 doi: 10.1109/TSMC.2016.2630800
    [124] Feng Z C, He W, Zhou Z J, Ban X J, Hu C H, Han X X. A new safety assessment method based on belief rule base with attribute reliability. IEEE/CAA Journal of Automatica Sinica, 2021, 8(11): 1774−1785 doi: 10.1109/JAS.2020.1003399
    [125] Zhao F J, Zhou Z J, Hu C H, Cao Y, Han X X, Feng Z C. A new safety assessment method based on evidential reasoning rule with a prewarning function. IEEE Access, 2018, 6: 31862−31871 doi: 10.1109/ACCESS.2018.2815631
    [126] Wenzel H. Monitoring based risk assessment and asset management of civil infrastructures. In: Proceedings of the Structural Health Monitoring. 2019.

    Wenzel H. Monitoring based risk assessment and asset management of civil infrastructures. In: Proceedings of the Structural Health Monitoring. 2019.
    [127] Adumene S, Islam R, Amin T, Nitonye S, Yazdi M, Johnson K T. Advances in nuclear power system design and fault-based condition monitoring towards safety of nuclear-powered ships. Ocean Engineering, 2022, 251: Article No. 111156 doi: 10.1016/j.oceaneng.2022.111156
    [128] Compare M, Martini F, Mattafirri S, Carlevaro F, Zio E. Semi-Markov model for the oxidation degradation mechanism in gas turbine nozzles. IEEE Transactions on Reliability, 2016, 65(2): 574−581 doi: 10.1109/TR.2015.2506610
    [129] Zadakbar O, Imtiaz S, Khan F. Dynamic risk assessment and fault detection using a multivariate technique. Process Safety Progress, 2013, 32(4): 365−375 doi: 10.1002/prs.11609
    [130] Yu H Y, Khan F, Garaniya V, Ahmad A. Self-organizing map based fault diagnosis technique for non-Gaussian processes. Industrial and Engineering Chemistry Research, 2014, 53(21): 8831−8843
    [131] Wang H Z, Khan F, Ahmed S, Imtiaz S. Dynamic quantitative operational risk assessment of chemical processes. Chemical Engineering Science, 2016, 142: 62−78 doi: 10.1016/j.ces.2015.11.034
    [132] Zeng Z G, Zio E. Dynamic risk assessment based on statistical failure data and condition-monitoring degradation data. IEEE Transactions on Reliability, 2018, 67(2): 609−622 doi: 10.1109/TR.2017.2778804
    [133] Zio E. Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering and System Safety, 2022, 218: Article No. 108119
    [134] Hu Y, Miao X W, Si Y, Pan E S, Zio E. Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering and System Safety, 2022, 217: Article No. 108063
    [135] Zhao S, Makis V, Chen S W, Li Y. Health assessment method for electronic components subject to condition monitoring and hard failure. IEEE Transactions on Instrumentation and Measurement, 2019, 68(1): 138−150 doi: 10.1109/TIM.2018.2839938
    [136] Dehghanian P, Guan Y F, Kezunovic M. Real-time life-cycle assessment of high-voltage circuit breakers for maintenance using online condition monitoring data. IEEE Transactions on Industry Applications, 2019, 55(2): 1135−1146 doi: 10.1109/TIA.2018.2878746
    [137] Kim H, Lee S H, Park J S, Kim H, Chang Y S, Heo G. Reliability data update using condition monitoring and prognostics in probabilistic safety assessment. Nuclear Engineering and Technology, 2015, 47(2): 204−211 doi: 10.1016/j.net.2014.12.008
    [138] BahooToroody A, Abaei M M, BahooToroody F, De Carlo F, Abbassi R, Khalaj S. A condition monitoring based signal filtering approach for dynamic time dependent safety assessment of natural gas distribution process. Process Safety and Environmental Protection, 2019, 123: 335−343 doi: 10.1016/j.psep.2019.01.016
    [139] Xing J D, Zeng Z G, Zio E. A framework for dynamic risk assessment with condition monitoring data and inspection data. Reliability Engineering and System Safety, 2019, 191: Article No. 106552
    [140] Ni M, McCalley J D, Vittal V, Tayyib T. Online risk-based security assessment. IEEE Transactions on Power Systems, 2003, 18(1): 258−265 doi: 10.1109/TPWRS.2002.807091
    [141] Li H F, Diao R S, Zhang X H, Lin X, Lu X, Shi D, et al. An integrated online dynamic security assessment system for improved situational awareness and economic operation. IEEE Access, 2019, 7: 162571−162582 doi: 10.1109/ACCESS.2019.2952178
    [142] Tchernykh A, Babenko M, Chervyakov N, Miranda-López V, Avetisyan A, Drozdov A Y, et al. Scalable data storage design for nonstationary IoT environment with adaptive security and reliability. IEEE Internet of Things Journal, 2020, 7(10): 10171−10188 doi: 10.1109/JIOT.2020.2981276
    [143] Sobajic D J, Pao Y H. Artificial neural-net based dynamic security assessment for electric power systems. IEEE Transactions on Power Systems, 1989, 4(1): 220−228 doi: 10.1109/59.32481
    [144] Sun K, Likhate S, Vittal V, Kolluri V S, Mandal S. An online dynamic security assessment scheme using phasor measurements and decision trees. IEEE Transactions on Power Systems, 2007, 22(4): 1935−1943 doi: 10.1109/TPWRS.2007.908476
    [145] Diao R S, Sun K, Vittal V, O'Keefe R J, Richardson M R, Bhatt N, et al. Decision tree-based online voltage security assessment using PMU measurements. IEEE Transactions on Power Systems, 2009, 24(2): 832−839 doi: 10.1109/TPWRS.2009.2016528
    [146] He M, Zhang J S, Vittal V. Robust online dynamic security assessment using adaptive ensemble decision-tree learning. IEEE Transactions on Power Systems, 2013, 28(4): 4089−4098 doi: 10.1109/TPWRS.2013.2266617
    [147] Zhang R, Xu Y. Data-driven dynamic security assessment and control of power systems: An online sequential learning method. Journal of Energy Engineering, 2019, 145(5): Article No. 04019019
    [148] Zhai C, Nguyen H D, Zong X F. Dynamic security assessment of small-signal stability for power grids using windowed online Gaussian process. IEEE Transactions on Automation Science and Engineering, 2023, 20(2): 1170−1179 doi: 10.1109/TASE.2022.3173368
    [149] Singh M, Chauhan S. A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems. Electric Power Systems Research, 2023, 214: Article No. 108923 doi: 10.1016/j.jpgr.2022.108923
    [150] Liu Z Y, Zhang Y, Ding Z J, He X. An online active broad learning approach for real-time safety assessment of dynamic systems in nonstationary environments. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(10): 6714−6724 doi: 10.1109/TNNLS.2022.3222265
    [151] Liu Z Y, He X. Real-time safety assessment for dynamic systems with limited memory and annotations. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(9): 10076−10086 doi: 10.1109/TITS.2023.3266256
    [152] Liu Z Y, He X. Dynamic submodular-based learning strategy in imbalanced drifting streams for real-time safety assessment in nonstationary environments. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3): 3038−3051 doi: 10.1109/TNNLS.2023.3294788
    [153] He X, Liu Z Y. Dynamic model interpretation-guided online active learning scheme for real-time safety assessment. IEEE Transactions on Cybernetics, 2024, 54(5): 2734−2745 doi: 10.1109/TCYB.2023.3339242
    [154] Li A Q, Ding Y L, Wang H, Guo T. Analysis and assessment of bridge health monitoring mass data—progress in research/development of "Structural Health Monitoring". Science China Technological Sciences, 2012, 55(8): 2212−2224 doi: 10.1007/s11431-012-4818-5
    [155] Bao Y Q, Beck J L, Li H. Compressive sampling for accelerometer signals in structural health monitoring. Structural Health Monitoring, 2011, 10(3): 235−246 doi: 10.1177/1475921710373287
    [156] Bao Y Q, Tang Z Y, Li H. Compressive-sensing data reconstruction for structural health monitoring: A machine-learning approach. Structural Health Monitoring, 2020, 19(1): 293−304 doi: 10.1177/1475921719844039
    [157] Harshitha C, Alapati M, Chikkakrishna N K. Damage detection of structural members using internet of things (IoT) paradigm. Materials Today: Proceedings, 2021, 43: 2337−2341 doi: 10.1016/j.matpr.2021.01.679
    [158] Abdelgawad A, Yelamarthi K. Internet of things (IoT) platform for structure health monitoring. Wireless Communications and Mobile Computing, 2017, 2017(1): Article No. 6560797
    [159] Goulet J A, Michel C, der Kiureghian A. Data-driven post-earthquake rapid structural safety assessment. Earthquake Engineering and Structural Dynamics, 2015, 44(4): 549−562
    [160] Catelani M, Ciani L, Galar D, Patrizi G. Optimizing maintenance policies for a yaw system using reliability-centered maintenance and data-driven condition monitoring. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9): 6241−6249 doi: 10.1109/TIM.2020.2968160
    [161] Nyman J, Rosengren P, Kool P, Karoumi R, Leander J, Petursson H. Smart condition monitoring of a steel bascule railway bridge. Life-Cycle of Structures and Infrastructure Systems. London: CRC Press, 2023. 229−236
    [162] Bandara R P, Chan T H T, Thambiratnam D P. Structural damage detection method using frequency response functions. Structural Health Monitoring, 2014, 13(4): 418−429 doi: 10.1177/1475921714522847
    [163] Avci O, Abdeljaber O, Kiranyaz S, Inman D. Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications. Structural Health Monitoring and Damage Detection, Volume 7. Cham: Springer, 2017. 49−54
    [164] Entezami A, Shariatmadar H. Structural health monitoring by a new hybrid feature extraction and dynamic time warping methods under ambient vibration and non-stationary signals. Measurement, 2019, 134: 548−568 doi: 10.1016/j.measurement.2018.10.095
    [165] Avendano-Valencia L D, Spiridonakos M D, Fassois S D. In-operation identification of a wind turbine structure via non-stationary parametric models. In: Proceedings of the 8th International Workshop on Structural Health Monitoring. Stanford, CA, USA: Stanford University, 2011. 2611
    [166] Xu C, Ni Y Q, Wang Y W. A novel Bayesian blind source separation approach for extracting non-stationary and discontinuous components from structural health monitoring data. Engineering Structures, 2022, 269: Article No. 114837 doi: 10.1016/j.engstruct.2022.114837
    [167] Ye X W, Xi P S, Su Y H. Analysis of non-stationary wind characteristics at an arch bridge using structural health monitoring data. Journal of Civil Structural Health Monitoring, 2017, 7(4): 573−587 doi: 10.1007/s13349-017-0244-5
    [168] Hua X, Xiao F, Chen G S, Zatar W, Hulsey L. Stochastic non-stationary characteristics of vehicle-induced bridge vibrations. Journal of Low Frequency Noise, Vibration and Active Control, 2023, 42(2): 759−770 doi: 10.1177/14613484221141800
    [169] Klischat M, Althoff M. Generating critical test scenarios for automated vehicles with evolutionary algorithms. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV). Paris, France: IEEE, 2019. 2352−2358
    [170] Feng S, Sun H W, Yan X T, Zhu H J, Zou Z X, Shen S Y, et al. Dense reinforcement learning for safety validation of autonomous vehicles. Nature, 2023, 615(7953): 620−627 doi: 10.1038/s41586-023-05732-2
    [171] Krajewski R, Moers T, Nerger D, Eckstein L. Data-driven maneuver modeling using generative adversarial networks and variational autoencoders for safety validation of highly automated vehicles. In: Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC). Maui, HI, USA: IEEE, 2018. 2383−2390
    [172] Jenkins I R, Gee L O, Knauss A, Yin H, Schroeder J. Accident scenario generation with recurrent neural networks. In: Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC). Maui, HI, USA: IEEE, 2018. 3340−3345
    [173] Wang C, Storms K, Winner H. Online safety assessment of automated vehicles using silent testing. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 13069−13083 doi: 10.1109/TITS.2021.3119546
    [174] Åsljung D, Nilsson J, Fredriksson J. Using extreme value theory for vehicle level safety validation and implications for autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 2017, 2(4): 288−297 doi: 10.1109/TIV.2017.2768219
    [175] Liu Z Y, He X. Contrastive preference-guided active learning approach based on ranking correlation for real-time safety assessment. IEEE Transactions on Automation Science and Engineering. DOI: 10.1109/TASE.2024.3401470
    [176] Fouad A A, Vekataraman S, Davis J A. An expert system for security trend analysis of a stability-limited power system. IEEE Transactions on Power Systems, 1991, 6(3): 1077−1084 doi: 10.1109/59.119249
    [177] Wehenkel L, Van Cutsem T, Ribbens-Pavella M. An artificial intelligence framework for online transient stability assessment of power systems. IEEE Transactions on Power Systems, 1989, 4(2): 789−800 doi: 10.1109/59.193853
    [178] Rovnyak S, Kretsinger S, Thorp J, Brown D. Decision trees for real-time transient stability prediction. IEEE Transactions on Power Systems, 1994, 9(3): 1417−1426 doi: 10.1109/59.336122
    [179] Kamwa I, Grondin R, Loud L. Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques. IEEE Transactions on Power Systems, 2001, 16(3): 526−536 doi: 10.1109/59.932291
    [180] Diao R S, Vittal V, Logic N. Design of a real-time security assessment tool for situational awareness enhancement in modern power systems. IEEE Transactions on Power Systems, 2010, 25(2): 957−965 doi: 10.1109/TPWRS.2009.2035507
    [181] Liu D, Niu D X, Wang H, Fan L L. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renewable Energy, 2014, 62: 592−597 doi: 10.1016/j.renene.2013.08.011
    [182] Sui Y, Ding R, Wang H Q. A novel approach for occupational health and safety and environment risk assessment for nuclear power plant construction project. Journal of Cleaner Production, 2020, 258: Article No. 120945 doi: 10.1016/j.jclepro.2020.120945
    [183] Shin J, Son H, Heo G. Cyber security risk evaluation of a nuclear I&C using BN and ET. Nuclear Engineering and Technology, 2017, 49(3): 517−524 doi: 10.1016/j.net.2016.11.004
    [184] Jang K B, Baek C H, Woo T H. Assessment for nuclear security using Analytic Hierarchy Process (AHP) incorporated with Neural Networking Method in nuclear power plants (NPPs). Kerntechnik, 2022, 87(5): 607−614 doi: 10.1515/kern-2022-0040
    [185] Cohn B, Noel T, Cardoni J, Haskin T, Osborn D, Aldemir T. Integrated safety and security analysis of nuclear power plants using dynamic event trees. Nuclear Science and Engineering, 2023, 197(sup1): S45−S56 doi: 10.1080/00295639.2023.2177076
    [186] Yockey P, Erickson A, Spirito C. Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants. Progress in Nuclear Energy, 2023, 166: Article No. 104960 doi: 10.1016/j.pnucene.2023.104960
    [187] Bajpai S, Sachdeva A, Gupta J P. Security risk assessment: Applying the concepts of fuzzy logic. Journal of Hazardous Materials, 2010, 173(1-3): 258−264 doi: 10.1016/j.jhazmat.2009.08.078
    [188] Zhou J F, Reniers G, Zhang L B. A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry. Chemical Engineering Science, 2017, 174: 136−145 doi: 10.1016/j.ces.2017.09.002
    [189] Peng T, Li C, Zhou X B. Application of machine learning to laboratory safety management assessment. Safety Science, 2019, 120: 263−267 doi: 10.1016/j.ssci.2019.07.007
    [190] Gao Y C, Zhang J C, Cui S X, Wu Y Q, Huang M L, Zhuang S L. Machine learning-based QSAR for safety evaluation of environmental chemicals. QSAR in Safety Evaluation and Risk Assessment. Academic Press, 2024. 89−99

    Gao Y C, Zhang J C, Cui S X, Wu Y Q, Huang M L, Zhuang S L. Machine learning-based QSAR for safety evaluation of environmental chemicals. QSAR in Safety Evaluation and Risk Assessment. Academic Press, 2024. 89−99
    [191] Wang Z H, Wen H Q, Su Y, Shen W F, Ren J Z, Ma Y J, et al. Insights into ensemble learning-based data-driven model for safety-related property of chemical substances. Chemical Engineering Science, 2022, 248: Article No. 117219 doi: 10.1016/j.ces.2021.117219
    [192] Amin T, Khan F. Dynamic process safety assessment using adaptive Bayesian network with loss function. Industrial and Engineering Chemistry Research, 2022, 61(45): 16799−16814
    [193] 胡昌华, 冯志超, 周志杰, 胡冠宇, 贺维, 曹友. 考虑环境干扰的液体运载火箭结构安全性评估方法. 中国科学: 信息科学, 2020, 50(10): 1559−1573 doi: 10.1360/SSI-2019-0148

    Hu Chang-Hua, Feng Zhi-Chao, Zhou Zhi-Jie, Hu Guan-Yu, He Wei, Cao You. A safety assessment method for a liquid launch rocket based on the belief rule base with environmental disturbance. Scientia Sinica Informationis, 2020, 50(10): 1559−1573 doi: 10.1360/SSI-2019-0148
    [194] Li Q Y, Wu Q G, Tu H Y, Zhang J P, Zou X, Huang S. Ground risk assessment for unmanned aircraft focusing on multiple risk sources in urban environments. Processes, 2023, 11(2): Article No. 542 doi: 10.3390/pr11020542
    [195] Tabassum A, Sabatini R, Gardi A. Probabilistic safety assessment for UAS separation assurance and collision avoidance systems. Aerospace, 2019, 6(2): Article No. 19 doi: 10.3390/aerospace6020019
    [196] Jiao R H, Peng K X, Zhang K, Ma L, Pi Y T. A novel scheme for remaining useful life prediction and safety assessment based on hybrid method. In: Proceedings of the CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). Xiamen, China: IEEE, 2019. 395−400
    [197] Dheedan A A. On-line safety monitor based on a safety assessment model and hierarchical deployment of a multi-agent system. International Journal on Advances in Internet Technology, 2012, 5(3-4): 95−113
    [198] Wang W X, Li X M, Xie L F, Lv H B, Lv Z H. Unmanned aircraft system airspace structure and safety measures based on spatial digital twins. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(3): 2809−2818 doi: 10.1109/TITS.2021.3108995
    [199] Farrar C R, Worden K. An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2007, 365(1851): 303−315 doi: 10.1098/rsta.2006.1928
    [200] Gordan M, Sabbagh-Yazdi S R, Ismail Z, Ghaedi K, Carroll P, McCrum D, et al. State-of-the-art review on advancements of data mining in structural health monitoring. Measurement, 2022, 193: Article No. 110939 doi: 10.1016/j.measurement.2022.110939
    [201] Cury A, Ribeiro D, Ubertini F, Todd M D. Structural Health Monitoring Based on Data Science Techniques. Cham: Springer, 2022.
    [202] Ko J M, Ni Y Q. Technology developments in structural health monitoring of large-scale bridges. Engineering Structures, 2005, 27(12): 1715−1725 doi: 10.1016/j.engstruct.2005.02.021
    [203] He Z G, Li W T, Salehi H, Zhang H, Zhou H Y, Jiao P C. Integrated structural health monitoring in bridge engineering. Automation in Construction, 2022, 136: Article No. 104168 doi: 10.1016/j.autcon.2022.104168
    [204] Niyirora R, Ji W, Masengesho E, Munyaneza J, Niyonyungu F, Nyirandayisabye R. Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: A systematic review. Results in Engineering, 2022, 16: Article No. 100761 doi: 10.1016/j.rineng.2022.100761
    [205] Kaartinen E, Dunphy K, Sadhu A. LiDAR-based structural health monitoring: Applications in civil infrastructure systems. Sensors, 2022, 22(12): Article No. 4610 doi: 10.3390/s22124610
    [206] Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289−1306 doi: 10.1109/TIT.2006.871582
    [207] Stepinski T, Uhl T, Staszewski W. Advanced Structural Damage Detection: From Theory to Engineering Applications. West Sussex: John Wiley & Sons, 2013.
    [208] Feng D M, Feng M Q. Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection-A review. Engineering Structures, 2018, 156: 105−117 doi: 10.1016/j.engstruct.2017.11.018
    [209] Worden K, Baldacchino T, Rowson J, Cross E J. Some recent developments in SHM based on nonstationary time series analysis. Proceedings of the IEEE, 2016, 104(8): 1589−1603 doi: 10.1109/JPROC.2016.2573596
    [210] Worden K, Iakovidis I, Cross E J. New results for the ADF statistic in nonstationary signal analysis with a view towards structural health monitoring. Mechanical Systems and Signal Processing, 2021, 146: Article No. 106979 doi: 10.1016/j.ymssp.2020.106979
    [211] Tarko A P. Surrogate measures of safety. Safe Mobility: Challenges, Methodology and Solutions. Leeds: Emerald Publishing Limited, 2018. 383−405
    [212] 宁滨. 智能交通中的若干科学和技术问题. 中国科学: 信息科学, 2018, 48(9): 1264−1269 doi: 10.1360/N112018-00080

    Ning Bin. A number of scientific and technical problems in intelligent transportation. Scientia Sinica Informationis, 2018, 48(9): 1264−1269 doi: 10.1360/N112018-00080
    [213] Arun A, Haque M, Bhaskar A, Washington S, Sayed T. A systematic mapping review of surrogate safety assessment using traffic conflict techniques. Accident Analysis and Prevention, 2021, 153: Article No. 106016
    [214] Riedmaier S, Ponn T, Ludwig D, Schick B, Diermeyer F. Survey on scenario-based safety assessment of automated vehicles. IEEE Access, 2020, 8: 87456−87477 doi: 10.1109/ACCESS.2020.2993730
    [215] Rassafi A A, Ganji S S, Pourkhani H. Road safety assessment under uncertainty using a multi attribute decision analysis based on Dempster-Shafer theory. KSCE Journal of Civil Engineering, 2018, 22(8): 3137−3152 doi: 10.1007/s12205-017-1854-5
    [216] de Leur P, Sayed T. Development of a road safety risk index. Transportation Research Record: Journal of the Transportation Research Board, 2002, 1784(1): 33−42 doi: 10.3141/1784-05
    [217] Wang W S, Wang L T, Zhang C Y, Liu C L, Sun L J. Social interactions for autonomous driving: A review and perspectives. Foundations and Trends® in Robotics, 2022, 10(3-4): 198−376
    [218] Morison K, Wang L, Kundur P. Power system security assessment. IEEE Power and Energy Magazine, 2004, 2(5): 30−39 doi: 10.1109/MPAE.2004.1338120
    [219] Grigsby L L. Dynamic security assessment. Power System Stability and Control. Boca Raton: CRC Press, 2007. 421−430
    [220] Alimi O A, Ouahada K, Abu-Mahfouz A M. A review of machine learning approaches to power system security and stability. IEEE Access, 2020, 8: 113512−113531 doi: 10.1109/ACCESS.2020.3003568
    [221] Fouad A A, Vittal V. Power System Transient Stability Analysis Using the Transient Energy Function Method. Englewood: Pearson, 1992.
    [222] Bellizio F, Cremer J L, Strbac G. Machine-learned security assessment for changing system topologies. International Journal of Electrical Power and Energy Systems, 2022, 134: Article No. 107380
    [223] Li Q Q, Xu Y, Ren C, Zhao J H. A hybrid data-driven method for online power system dynamic security assessment with incomplete PMU measurements. In: Proceedings of the IEEE Power and Energy Society General Meeting (PESGM). Atlanta, GA, USA: IEEE, 2019. 1−5
    [224] Makarov Y V, Du P W, Lu S, Nguyen T B, Guo X X, Burns J W, et al. PMU-based wide-area security assessment: Concept, method, and implementation. IEEE Transactions on Smart Grid, 2012, 3(3): 1325−1332 doi: 10.1109/TSG.2012.2193145
    [225] Jardim J L. Online dynamic security assessment. Real-Time Stability in Power Systems: Techniques for Early Detection of the Risk of Blackout. Cham: Springer, 2014. 159−197
    [226] Vaahedi E, Mansour Y, Tse E K. A general purpose method for on-line dynamic security assessment. IEEE Transactions on Power Systems, 1998, 13(1): 243−249 doi: 10.1109/59.651642
    [227] Zhang Y, Xie L. Online dynamic security assessment of microgrid interconnections in smart distribution systems. IEEE Transactions on Power Systems, 2015, 30(6): 3246−3254 doi: 10.1109/TPWRS.2014.2374876
    [228] Zhang Y C, Xu Y, Bu S Q, Dong Z, Zhang R. Online power system dynamic security assessment with incomplete PMU measurements: A robust white-box model. IET Generation, Transmission and Distribution, 2019, 13(5): 662−668
    [229] Liu R D, Verbič G, Ma J. A new dynamic security assessment framework based on semi-supervised learning and data editing. Electric Power Systems Research, 2019, 172: 221−229 doi: 10.1016/j.jpgr.2019.03.009
    [230] Zhang Y Q, Zhao Q, Tan B D, Yang J. A power system transient stability assessment method based on active learning. The Journal of Engineering, 2021, 2021(11): 715−723 doi: 10.1049/tje2.12068
    [231] Ren C, Xu Y. Transfer learning-based power system online dynamic security assessment: Using one model to assess many unlearned faults. IEEE Transactions on Power Systems, 2020, 35(1): 821−824 doi: 10.1109/TPWRS.2019.2947781
    [232] C˘C˘epin M. Event tree analysis. Assessment of Power System Reliability: Methods and Applications. London: Springer, 2011. 89−99
    [233] Bajpai S, Gupta J P. Site security for chemical process industries. Journal of Loss Prevention in the Process Industries, 2005, 18(4−6): 301−309 doi: 10.1016/j.jlp.2005.06.011
  • 加载中
图(5) / 表(5)
计量
  • 文章访问数:  307
  • HTML全文浏览量:  118
  • PDF下载量:  89
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-02-26
  • 录用日期:  2024-07-23
  • 网络出版日期:  2024-09-05

目录

    /

    返回文章
    返回