Reliability Assessment Model for Industrial Control System Based on Belief Rule Base


  • Yuhe Wang School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Peili Qiao School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Zhiyong Luo School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Guanglu Sun School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Guangze Wang School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China


Belief rule base (BRB), industrial control system (ICS), evidential reasoning (ER), reliability assessment, covariance matrix adaptation evolution strategy (CMA-ES) algorithm


This paper establishes a novel reliability assessment method for industrial control system (ICS). Firstly, the qualitative and quantitative information were integrated by evidential reasoning(ER) rule. Then, an ICS reliability assessment model was constructed based on belief rule base (BRB). In this way, both expert experience and historical data were fully utilized in the assessment. The model consists of two parts, a fault assessment model and a security assessment model. In addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (CMA-ES) algorithm, making the proposed model in line with the actual situation. Finally, the proposed model was compared with two other popular prediction methods through case study. The results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex ICSs.


Alambeigi, F.; Wang, Z.; Hegeman, R. (2019). Autonomous data-driven manipulation of unknown anisotropic deformable tissues using unmodelled continuum manipulators, IEEE Robotics and Automation Letters, 4(2), 254-261, 2019.

Brereton, R. G. (2016). Hotelling's T squared distribution, its relationship to the F distribution and its use in multivariate space, Journal of Chemometrics, 30(1), 18-21, 2016.

Chen, Q.; Abercrombie, R. K.; Sheldon, F. T. (2015). Risk assessment for industrial control systems quantifying availability using mean failure cost (MFC), Journal of Artificial Intelligence and Soft Computing Research, 5(3), 205-220, 2015.

Franco, I. C.; Schmitz, J. E.; Costa, T. V. (2017). Development of a predictive control based on Takagi-Sugeno model applied in a nonlinear system of industrial refrigeration, Chemical Engineering Communications, 204(1), 39-54, 2017.

Goedhart, R.; Schoonhoven, M.; Ronald, J. M. M. (2016). Correction factors for Shewhart and control charts to achieve desired unconditional ARL, International Journal of Production Research, 54(24), 7464-7479, 2016.

He, W.; Hu, G. Y.; Zhou, Z. J. (2018). A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network, Microelectronics Reliability, 87, 33-51, 2018.

Hu, G. Y.; Zhou, Z. J.; Zhang B. C. (2016). A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm, Applied Soft Computing, 48(C), 404-418, 2016.

Jin, L. J.; Peng, C. Y.; Jiang, T. (2017). System-level electric field exposure assessment by the fault tree analysis, IEEE Transactions on Electromagnetic Compatibility, 59(4), 1095- 1102, 2017.

Kriaa, S.; Pietre, L.; Bouissou, M. (2015). A survey of approaches combining safety and security for industrial control systems, Reliability Engineering & System Safety, 139, 156- 178, 2015.

Lee, Y. S.; Kim, D. J.; Kim, J. O. (2011). New FMECA methodology using structural importance and fuzzy theory, IEEE Transactions on Power Systems, 26(4), 2364-2370, 2011.

Liu, Z.; Liu, T.; Han, J. (2017). Signal model-based fault coding for diagnostics and prognostics of analog electronic circuits, IEEE Transactions on Industrial Electronics, PP(99), 1-1, 2017.

Luo, Z. Y.; Wang, P.; You, B. (2016). Serial reduction optimization research of complex product workflow's accuracy under the time constraint, Advances in Mechanical Engineering, 8(10), 1-9, 2016.

Luo, Z. Y.; You, B.; Liu, J. H. (2016). Research of the intrusion tolerance state transition system based on semi-markov, Transactions of Beijing Institute of Technology, 36(7), 712- 717, 2016.

Luo, Z. Y.; You, B.; Xu, Z. B. (2014). Automatic recognition model of intrusive intention based on three layers attack graph, Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 44(5), 1392-1397, 2014.

Navarro, A. D.; Yip, H. M.; Wang, Z. (2016). Automatic 3-d manipulation of soft objects by robotic arms with an adaptive deformation model, IEEE Transactions on Robotics, 32(2), 429-441, 2016.

Pacella, M. (2018). Unsupervised classification of multichannel profile data using PCA: An application to an emission control system, Computers & Industrial Engineering, 122, 161- 169, 2018.

Rusmini, P.; Crippa, V.; Cristofani, R. (2016). The role of the protein quality control system in SBMA, Journal of Molecular Neuroscience, 58(3), 348-364, 2016.

Sang, W.; Livne, E. (2016). Probabilistic aeroservoelastic reliability assessment considering control system component uncertainty, Aiaa Journal, 54(8), 2507-2520, 2016.

Thomas, M. C.; Zhu, W.; Romagnoli, J. A. (2017). Data mining and clustering in chemical process databases for monitoring and knowledge discovery, Journal of Process Control, S095915241730032X, 2017.

Wang, Z.; Li, P.; Navarro, A. D. (2015). Design and control of a novel multi-state compliant safe joint for robotic surgery, IEEE International Conference on Robotics and Automation (ICRA), 1023-1028, 2015.

Wang, Z.; Yip, H. M.; Navarro, A. D. (2016). Design of a novel compliant safe robot joint with multiple working states, IEEE/ASME Transactions on Mechatronics, 21(2), 1193-1198, 2016.

Zhou, Z. J.; Hu, G. Y.; Zhou, Z. J. (2017). A Model for hidden behavior prediction of complex systems based on belief rule base and power set, IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP(99), 1-7, 2017.



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