A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 11
Nov.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Zhichao Feng, Wei He, Zhijie Zhou, Xiaojun Ban, Changhua Hu and Xiaoxia Han, "A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1774-1785, Nov. 2021. doi: 10.1109/JAS.2020.1003399
Citation: Zhichao Feng, Wei He, Zhijie Zhou, Xiaojun Ban, Changhua Hu and Xiaoxia Han, "A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1774-1785, Nov. 2021. doi: 10.1109/JAS.2020.1003399

A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability

doi: 10.1109/JAS.2020.1003399
Funds:  This work was supported in part by the National Natural Science Foundation of China (61833016, 61751304, 61873273, 61702142, 61773388), the Key Research and Development Plan of Hainan (ZDYF2019007), and Shaanxi Outstanding Youth Science Foundation (2020JC-34)
More Information
  • Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.

     

  • loading
  • [1]
    K. Zhong, M. Han, and B. Han, “Data-driven based fault prognosis for industrial systems: A concise overview,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 330–345, Mar. 2020.
    [2]
    X. G. Wang, X. Y. Liu, and Y. Li, “An incremental model transfer method for complex process fault diagnosis,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1268–1280, Sept. 2019. doi: 10.1109/JAS.2019.1911618
    [3]
    Q. Q. Wang, Subway Crowded and Stampede Neural Networks Safety Assessment Basing on SHEL Model. Springer Singapore, 2016.
    [4]
    X. Q. Guo, S. Sui, B. C. Wang, and W. L. Zhang, “A current-based approach for short-circuit fault diagnosis in closed-loop current source inverter,” IEEE Trans. Industrial Electronics, vol. 67, no. 9, pp. 7941–7950, Sept. 2020. doi: 10.1109/TIE.2019.2941143
    [5]
    C. H. Lee and A. P. S. Meliopoulos, “Safety assessment of AC grounding systems based on voltage-dependent body resistance,” IEEE Trans. Industry Applications, vol. 51, no. 6, pp. 5204–5211, Nov–Dec. 2015. doi: 10.1109/TIA.2015.2412511
    [6]
    B. Lacevic, P. Rocco, and A. M. Zanchettin, “Safety assessment and control of robotic manipulators using danger field,” IEEE Trans. Robotics, vol. 29, no. 5, pp. 1257–1270, Oct. 2013. doi: 10.1109/TRO.2013.2271097
    [7]
    M. L. Fravolini and G. Campa, “Design of a neural network adaptive controller via a constrained invariant ellipsoids technique,” IEEE Trans. Neural Networks, vol. 22, no. 4, pp. 627–638, Apr. 2011. doi: 10.1109/TNN.2011.2111385
    [8]
    M. Ahmadi, A. Israel, and U. Topcu, “Safety assessemt based on physically-viable data-driven models,” in Proc. 56th IEEE Annual Conf. Decision and Control (CDC) Melbourne, VIC, Australia: IEEE, Dec. 2017, pp. 6409–6414.
    [9]
    B. Syd Ali, W. Ochieng, W. Schuster, A. Majumdar, and T. Chiew, “A safety assessment framework for the automatic dependent surveillance broadcast (ADS-B) system,” Safety Science, vol. 78, pp. 91–100, Oct. 2015. doi: 10.1016/j.ssci.2015.04.011
    [10]
    Z. J. Zhou, G. Y. Hu, C. H. Hu, C. L. Wen, and L. L. Chang, “A survey of belief rule base expert system,” IEEE Trans. System,Man and Cybernetics:Systems, 2019. DOI: 10.1109/tsmc.2019.2944893.
    [11]
    H. Badihi, Y. M. Zhang, and H. Hong, “Wind turbine fault diagnosis and fault-tolerant torque load control against actuator faults,” IEEE Trans. Control Systems Technology, vol. 23, no. 4, pp. 1351–1372, Jul. 2015. doi: 10.1109/TCST.2014.2364956
    [12]
    H. J. Zimmermann, Fuzzy Sets, Decision Making, and Expert Systems. Springer Science & Business Media, 2012, vol. 10.
    [13]
    P. Walley, “Measures of uncertainty in expert systems,” Artificial Intelligence, vol. 83, no. 1, pp. 1–58, May 1996. doi: 10.1016/0004-3702(95)00009-7
    [14]
    J. B. Yang, J. Liu, J. Wang, H. S. Sii, and H. W. Wang, “Belief rule-base inference methodology using the evidential reasoning approach-rimer,” IEEE Trans. Systems,Man,and Cybernetics-part A:Systems and Humans, vol. 36, no. 2, pp. 266–285, Mar. 2006. doi: 10.1109/TSMCA.2005.851270
    [15]
    Z. C. Feng, Z. J. Zhou, C. H. Hu, L. L. Chang, G. Y. Hu, and F. J. Zhao, “A new belief rule base model with attribute reliability,” IEEE Trans. Fuzzy Systems, vol. 27, no. 5, pp. 903–916, May 2019.
    [16]
    Z. G. Liu, Q. Pan, J. Dezert, and G. Mercier, “Hybrid classification system for uncertain data,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 47, no. 10, pp. 2783–2790, Oct. 2017.
    [17]
    Z. G. Liu, Q. Pan, J. Dezert, and A. Martin, “Combination of classifiers with optimal weight based on evidential reasoning,” IEEE Trans. Fuzzy Systems, vol. 26, no. 3, pp. 1217–1230, Jun. 2018.
    [18]
    Z. G. Liu, Q. Pan, J. Dezert, J. W. Han, and Y. He, “Classifier fusion with contextual reliability evaluation,” IEEE Trans. Cybernetics, vol. 48, no. 5, pp. 1605–1618, May 2018.
    [19]
    L. L. Chang, Z. J. Zhou, Y. W. Chen, T. J. Liao, Y. Hu, and L. H. Yang, “Belief rule base structure and parameter joint optimization under disjunctive assumption for nonlinear complex system modeling,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 48, no. 9, pp. 1542–1554, Sept. 2018.
    [20]
    Y. W. Chen, J. B. Yang, D. L. Xu, and S. L. Yang, “On the inference and approximation properties of belief rule based systems,” Information Sciences, vol. 234, pp. 121–135, Jun. 2013. doi: 10.1016/j.ins.2013.01.022
    [21]
    Y. W. Chen, J. B. Yang, C. C. Pan, D. L. Xu, and Z. J. Zhou, “Identification of uncertain nonlinear systems: Constructing belief rulebased models,” Knowledge-Based Systems, vol. 73, pp. 124–133, Jan. 2015. doi: 10.1016/j.knosys.2014.09.010
    [22]
    Z. J. Zhou, G. Y. Hu, C. H. Hu, C. L. Wen, and L. L. Chang, “A survey of belief rule-base expert system,” IEEE Trans. Systems,Man,and Cybernetics:Systems, 2019. DOI: 10.1109/TSMC.2019.2944893.
    [23]
    G. L. Kong, D. L. Xu, J. B. Yang, X. F. Yin, T. B. Wang, B. G. Jiang, and Y. H. Hu, “Belief rule-based inference for predicting trauma outcome,” Knowledge-Based Systems, vol. 95, pp. 35–44, Mar. 2016. doi: 10.1016/j.knosys.2015.12.002
    [24]
    B. Li, H. W. Wang, J. B. Yang, M. Guo, and C. Qi, “A belief-rule-based inference method for aggregate production planning under uncertainty,” Int. J. Production Research, vol. 51, no. 1, pp. 83–105, Jan. 2013. doi: 10.1080/00207543.2011.652262
    [25]
    Z. G. Zhou, F. Liu, L. L. Li, L. C. Jiao, Z. J. Zhou, J. B. Yang, and Z. L. Wang, “A cooperative belief rule based decision support system for lymph node metastasis diagnosis in gastric cancer,” Knowledge-Based Systems, vol. 85, pp. 62–70, Sept. 2015. doi: 10.1016/j.knosys.2015.04.019
    [26]
    L. H. Yang, Y. M. Wang, J. Liu, and L. Martínez, “A joint optimization method on parameter and structure for belief-rule-based systems,” Knowledge-Based Systems, vol. 142, pp. 220–240, Feb. 2018. doi: 10.1016/j.knosys.2017.11.039
    [27]
    D. L. Xu, J. Liu, J. B. Yang, G. P. Liu, J. Wang, I. Jenkinson, and J. Ren, “Inference and learning methodology of belief-rule-based expert system for pipeline leak detection,” Expert Systems with Applications, vol. 32, no. 1, pp. 103–113, Jan. 2007. doi: 10.1016/j.eswa.2005.11.015
    [28]
    J. B. Yang, J. Liu, D. L. Xu, J. Wang, and H. W. Wang, “Optimization models for training belief-rule-based systems,” IEEE Trans. Systems,Man,and Cybernetics—Part A:Systems and Humans, vol. 37, no. 4, pp. 569–585, Jul. 2007. doi: 10.1109/TSMCA.2007.897606
    [29]
    X. J. Xu, X. P. Yan, C. X. Sheng, C. Q. Yuan, D. L. Xu, and J. B. Yang, “A belief rulebased expert system for fault diagnosis of marine diesel engines,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 50, no. 2, pp. 656–672, Feb. 2020.
    [30]
    Z. J. Zhou, G. Y. Hu, B. C. Zhang, C. H. Hu, Z. G. Zhou, and P. L. Qiao, “A model for hidden behavior prediction of complex systems based on belief rule base and power set,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 48, no. 9, pp. 1649–1655, Sept. 2018. doi: 10.1109/TSMC.2017.2665880
    [31]
    J. B. Sun, J. X. Huang, L. L. Chang, J. Jiang, and Y. J. Tan, “Brbcast: A new approach to belief rule-based system parameter learning via extended causal strength logic,” Information Sciences, vol. 444, pp. 51–71, May 2018. doi: 10.1016/j.ins.2018.02.055
    [32]
    L. M. Jiao, T. Denoeux, and Q. Pan, “A hybrid belief rule-based classification system based on uncertain training data and expert knowledge,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 46, no. 12, pp. 1711–1723, Dec. 2016.
    [33]
    J. B. Yang and D. L. Xu, “Evidential reasoning rule for evidence combination,” Artificial Intelligence, vol. 205, pp. 1–29, Dec. 2013. doi: 10.1016/j.artint.2013.09.003
    [34]
    Y. Yang, C. Fu, Y. W. Chen, D. L. Xu, and S. L. Yang, “A belief rule based expert system for predicting consumer preference in new product development,” Knowledge-Based Systems, vol. 94, pp. 105–113, Feb. 2016. doi: 10.1016/j.knosys.2015.11.012
    [35]
    L. H. Yang, Y. M. Wang, and Y. G. Fu, “A consistency analysisbased rule activation method for extended belief-rule-based systems,” Information Sciences, no. 445–446, pp. 50–65, Jun. 2018.
    [36]
    A. Faust, P. Ruymgaart, M. Salman, R. Fierro, and L. Tapia, “Continuous action reinforcement learning for control-affine systems with unknown dynamics,” IEEE/CAA J. Autom. Sinica, vol. 1, no. 3, pp. 323–336, Jul. 2014. doi: 10.1109/JAS.2014.7004690

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(6)

    Article Metrics

    Article views (936) PDF downloads(55) Cited by()

    Highlights

    • Safety assessment under environment interference
    • Complex system modeling based on observation data and expert knowledge
    • A new calculation method for belief rule reliability is developed where the input reliability and uncertain expert knowledge are both considered
    • A new optimization method to reduce the complexity of the belief rule base expert system and improve its modeling accuracy

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return