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摘要: 综合考虑动态系统历史记录、当前状态以及未来退化趋势信息来对其安全性进行在线评估是极其重要的.本文提出了一种基于证据推理（Evidential reasoning，ER）的安全性在线评估方法.该方法先融合多个安全性指标获得各个时刻的安全性状态，而后融合系统"历史"、"当前"、"未来"时刻的安全性状态，评估得到系统的综合安全性水平.首先，建立了基于三阶Volterra滤波器的在线预测模型，预测指标未来信息；然后，建立了指标最优自适应权重求取模型，计算并更新指标实时权重；最后，提出了基于证据推理方法的融合框架，对"历史"、"当前"、"未来"时刻的信息进行融合，得到系统当前时刻的综合安全性评估结果.通过对某惯性平台系统的安全性评估实例验证了所提方法的有效性.Abstract: It is of great significance to online assess the safety of a dynamic system by taking into account historical records, current state, and degradation trend. This paper proposes a new safety assessment method based on the evidential reasoning (ER) approach. To obtain the integrated safety level, multiple safety indicators are fused at first and the "history", "current" and "future" safety states are then integrated. Firstly, a forecasting model based on a third-order Volterra filter is proposed to online predict the safety indicators' information. Secondly, an optimal adaptive fusion weighting model is developed to calculate and automatically update the weighing coefficient. Finally, a safety assessment aggregation scheme based on the ER approach is presented to fuse the "history", "current" and "future" safety information synthetically to obtain a comprehensive safety assessment result of the dynamic system. A practical example of the inertial platform is studied to validate the effectiveness of the proposed ER-based safety assessment method.
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Key words:
- Dynamic system /
- safety /
- online assessment /
- evidential reasoning (ER) /
- Volterra filter /
- weight /
- information fusion
1) 本文责任编委 周东华 -
图 2 新的在线安全性评估方法实现步骤
Fig. 2 Implementation steps of the new online safety assessment method
图 7 惯性平台系统安全性状态的分布式评估结果
Fig. 7 Distributed safety state results of the inertial platform system
图 8 惯性平台系统安全性分布式评估结果
Fig. 8 Distributed safety assessment results of the inertial platform system
表 1 漂移系数评估等级对应的参考点
Table 1 The referential points of drift coefficients
语义值 ${F_1}$ ${F_2}$ ${F_3}$ ${K_0}$对应的效用(d/h) 0.02 0.04 0.06 ${K_1}$对应的效用(d/h*g) 0.015 0.03 0.05 
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[1] 周东华, 史建涛, 何潇.动态系统间歇故障诊断技术综述.自动化学报, 2014, 40(2):161-171 http://www.aas.net.cn/CN/abstract/abstract18279.shtmlZhou Dong-Hua, Shi Jian-Tao, He Xiao. Review of intermittent fault diagnosis techniques for dynamic systems. Acta Automatica Sinica, 2014, 40(2):161-171 http://www.aas.net.cn/CN/abstract/abstract18279.shtml [2] 李文博, 王大轶, 刘成瑞.动态系统实际故障可诊断性的量化评价研究.自动化学报, 2015, 41(3):497-507 http://www.aas.net.cn/CN/abstract/abstract18628.shtmlLi Wen-Bo, Wang Da-Yi, Liu Cheng-Rui. Quantitative evaluation of actual fault diagnosability for dynamic systems. Acta Automatica Sinica, 2015, 41(3):497-507 http://www.aas.net.cn/CN/abstract/abstract18628.shtml [3] 司小胜, 胡昌华, 周志杰.基于证据推理的故障预报模型.中国科学:信息科学, 2010, 40(7):954-967Si Xiao-Sheng, Hu Chang-Hua, Zhou Zhi-Jie. Fault prediction model based on evidential reasoning approach. Science China Information Sciences, 2010, 40(7):954-967 [4] Siu N. Risk assessment for dynamic systems:an overview. Reliability Engineering & System Safety, 1994, 43(1):43-73 http://d.wanfangdata.com.cn/OAPaper/oai_doaj-articles_ba2355fe34705770023b1e5ffac29588 [5] Eryilmaz S. Dynamic assessment of multi-state systems using phase-type modeling. Reliability Engineering and System Safety, 2015, 140:71-77 doi: 10.1016/j.ress.2015.03.037 [6] Wu D D, Chen S H, Olson D L. Business intelligence in risk management:some recent progresses. Information Sciences, 2014, 256:1-7 doi: 10.1016/j.ins.2013.10.008 [7] Zhou Z J, Chang L L, Hu C H, Han X X, Zhou Z G. A new BRB-ER-based model for assessing the lives of products using both failure data and expert knowledge. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2016, 46(11):1529-1543 doi: 10.1109/TSMC.2015.2504047 [8] Vališ. Contribution to reliability and safety assessment of systems. Safety & Reliability, 2007, 27(3):23-35 http://d.wanfangdata.com.cn/OAPaper/oai_doaj-articles_96e6a29d79409e9369683e36726e859a [9] 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 [10] Stamatelatos M, Dezfuli H, Apostolakis G, Everline C, Guarro S, Mathias D, Mosleh A, Paulos T, Riha D, Smith C, Vesely W, Youngblood R. Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners, NASA/SP-2011-3421, NASA Center for AeroSpace Information, 2011. [11] Fullwood R R. Probabilistic Safety Assessment in the Chemical and Nuclear Industries. Boston, MA:Butterworth-Heinemann, 2000. [12] Durga Rao K, Gopika V, Sanyasi Rao V V S, Kushwahaa H S, Vermab A K, Srividyab A. Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliability Engineering & System Safety, 2009, 94(4):872-883 [13] Verma A K, Ajit S, Karanki D R. Probabilistic safety assessment. Reliability and Safety Engineering (Second edition). London:Springer-Verlag, 2016. 333-372 http://d.wanfangdata.com.cn/Periodical/zngydxxb-e201105043 [14] 王晨. 基于状态监测的输变电设备状态评估及故障预警[硕士学位论文], 华北电力大学, 中国, 2015Wang Chen. Condition Assessment of Transmission Equipment based on Condition Monitoring and Failure Alert[Master dissertation], School of Electrical and Electronic Engineering, China, 2015 [15] 江寅虎. 基于状态监测的旋转部件可靠性评估方法研究[硕士学位论文], 大连理工大学, 中国, 2014Jiang Yin-Hu. Reliability Evaluation Method Research based on Condition Monitoring for Rotating Parts[Master dissertation], Dalian University of Technology, China, 2014 [16] 万明杰, 周光辉, 程元森, 靳小莉.基于运行状态信息的数控珩磨机液压系统可靠性预测方法.应用科技, 2013, 39(6):30-33 http://d.wanfangdata.com.cn/Periodical/yykj201206006Fang Ming-Jie, Zhou Guang-Hui, Chen Yuan-Sen, Jin Xiao-Li. Reliability prediction for hydraulic system of CNC honing machine based on the operational status information. Applied Science and Technology, 2013, 39(6):30-33 http://d.wanfangdata.com.cn/Periodical/yykj201206006 [17] Hermans E, Van den Bossche F, Wets G. Combining road safety information in a performance index. Accident Analysis & Prevention, 2008, 40(4):1337-1344 [18] Yang J B, Liu J, Wang J, Sii H S, Wang H W. Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2006, 36(2):266-285 doi: 10.1109/TSMCA.2005.851270 [19] Yang J B, Wang Y M, Xu D L, Chin K S. The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties. European Journal of Operational Research, 2006, 171(1):309-343 doi: 10.1016/j.ejor.2004.09.017 [20] Zhou Z J, Hu C H, Yang J B, Xu D L, Zhou D H. Online updating belief-rule-base using the RIMER approach. IEEE Transactions on System, Man, and Cybernetics-Part A:Systems and Humans, 2011, 41(6):1225-1243 doi: 10.1109/TSMCA.2011.2147312 [21] Zhou Z J, Hu C H, Zhang B C, Xu D L, Chen Y W. Hidden behavior prediction of complex systems based on hybrid information. IEEE Transactions on Cybernetics, 2013, 43(2):402-411 doi: 10.1109/TSMCB.2012.2208266 [22] 王敏. 大电网在线安全评估的理论与方法研究[硕士学位论文], 华中科技大学, 中国, 2013Wang Min. Research on Theory and Methods of Online Dynamic Security Assessment of Large Scale Power System[Master dissertation], Huazhong University of Science & Technology, China, 2013 [23] Wang Y M, Elhag T M S. Evidential reasoning approach for bridge condition assessment. Expert Systems with Applications, 2008, 34(1):689-699 doi: 10.1016/j.eswa.2006.10.006 [24] Nowak R D, Van Veen B D. Volterra filter equalization:a fixed point approach. IEEE Transactions on Signal Processing, 1997, 45(2):377-388 doi: 10.1109/78.554302 [25] 韦保林, 罗晓曙, 王秉宏, 全宏俊, 郭伟, 傅金阶.一种基于三阶Volterra滤波器的混沌时间序列自适应预测方法.物理学报, 2002, 51(10):2205-2210 doi: 10.3321/j.issn:1000-3290.2002.10.007Wei Bao-Lin, Luo Xiao-Shu, Wang Bing-Hong, Quan Hong-Jun, Guo Wei, Fu Jin-Jie. A method based on the third-order Volterra filter for adaptive predictions of chaotic time series. Acta Physica Sinica, 2002, 51(10):2205-2210 doi: 10.3321/j.issn:1000-3290.2002.10.007 [26] 张家树, 肖先赐.用于混沌时间序列自适应预测的一种少参数二阶Volterra滤波器.物理学报, 2001, 50(7):1248-1254 doi: 10.7498/aps.50.1248Zhang Jia-Shu, Xiao Xian-Ci. A reduced parameter second-order Volterra filter with application to nonlinear adaptive prediction of chaotic time series. Acta Physica Sinica, 2001, 50(7):1248-1254 doi: 10.7498/aps.50.1248 [27] Kalluri S, Arce G R. A general class of nonlinear normalized adaptive filtering algorithms. IEEE Transactions on Signal Processing, 1999, 47(8):2262-2272 doi: 10.1109/78.774769 [28] Yang J B, Xu D L. Evidential reasoning rule for evidence combination. Artificial Intelligence, 2013, 205:1-29 doi: 10.1016/j.artint.2013.09.003 [29] Lo T P, Guo S J. Effective weighting model based on the maximum deviation with uncertain information. Expert Systems with Applications, 2010, 37(12):8445-8449 doi: 10.1016/j.eswa.2010.05.034 [30] Sheret M. The coefficient of variation:weighting considerations. Social Indicators Research, 1984, 15(3):289-295 doi: 10.1007/BF00668675 [31] Yang J B. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. European Journal of Operational Research, 2001, 131(1):31-61 doi: 10.1016/S0377-2217(99)00441-5 [32] Wang Y M, Yang J B, Xu D L. Environmental impact assessment using the evidential reasoning approach. European Journal of Operational Research, 2006, 174(3):1885-1913 doi: 10.1016/j.ejor.2004.09.059 [33] Zhou Z J, Hu C H, Wang W B, Zhang B C, Xu D L, Zheng J F. Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base. Expert Systems with Applications, 2012, 39(6):6140-6149 doi: 10.1016/j.eswa.2011.11.068 [34] Yang J B, Xu D L. Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics——Part A:Systems and Humans, 2002, 32(3):376-393 doi: 10.1109/TSMCA.2002.802809 [35] 宋亚飞, 王晓丹, 雷蕾, 邢雅琼.基于证据理论和混淆矩阵的传感器可靠性评估.控制与决策, 2015, 30(6):1111-1115 http://d.wanfangdata.com.cn/Periodical/kzyjc201506022Song Ya-Fei, Wang Xiao-Dan, Lei Lei, Xing Ya-Qiong. Evaluating dynamic reliability of sensors based on evidence theory and confusion matrix. Control and Decision, 2015, 30(6):1111-1115 http://d.wanfangdata.com.cn/Periodical/kzyjc201506022 [36] Elouedi Z, Mellouli K, Smets P. Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004, 34(1):782-787 doi: 10.1109/TSMCB.2003.817056 [37] 徐晓滨, 王玉成, 文成林.评估诊断证据可靠性的信息融合故障诊断方法.控制理论与应用, 2011, 28(4):504-510 http://d.wanfangdata.com.cn/Periodical/kzllyyy201104010Xu Xiao-Bin, Wang Yu-Cheng, Wen Cheng-Lin. Information-fusion method for fault diagnosis based on reliability evaluation of evidence. Control Theory & Applications, 2011, 28(4):504-510 http://d.wanfangdata.com.cn/Periodical/kzllyyy201104010 -
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