Research article Special Issues

Reliability analysis and resilience measure of complex systems in shock events

  • Received: 23 July 2023 Revised: 25 September 2023 Accepted: 12 October 2023 Published: 16 October 2023
  • The working environment of complex systems is complex and variable, and their performance is often affected by various shock events during the service phase. In this paper, first, considering that the system performance will be affected by shocks again in the process of maintenance, the reliability changes and fault process of complex systems are discussed. Second, the performance change processes of complex systems are analyzed under multiple shocks and maintenance. Then, based on performance loss and recovery, this paper analyzes the reliability and resilience of complex systems under the intersecting process of multiple shocks and maintenance. Considering the direct and indirect losses caused by shocks, as well as maintenance costs, the changes in total costs are analyzed. Finally, the practicability of the proposed model is checked by using a specific welding robot system.

    Citation: Hongyan Dui, Huiting Xu, Haohao Zhou. Reliability analysis and resilience measure of complex systems in shock events[J]. Electronic Research Archive, 2023, 31(11): 6657-6672. doi: 10.3934/era.2023336

    Related Papers:

  • The working environment of complex systems is complex and variable, and their performance is often affected by various shock events during the service phase. In this paper, first, considering that the system performance will be affected by shocks again in the process of maintenance, the reliability changes and fault process of complex systems are discussed. Second, the performance change processes of complex systems are analyzed under multiple shocks and maintenance. Then, based on performance loss and recovery, this paper analyzes the reliability and resilience of complex systems under the intersecting process of multiple shocks and maintenance. Considering the direct and indirect losses caused by shocks, as well as maintenance costs, the changes in total costs are analyzed. Finally, the practicability of the proposed model is checked by using a specific welding robot system.



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    [1] X. Ma, K. S. Trivedi, Reliability and performance of general two-dimensional broadcast wireless network, Perform. Eval., 95 (2016), 41–59. https://doi.org/10.1016/j.peva.2015.09.005
    [2] X. Zhao, S. Wang, X. Wang, K. Cai, A multi-state shock model with mutative failure patterns, Reliab. Eng. Syst. Saf., 178 (2018), 1–11. https://doi.org/10.1016/j.ress.2018.05.014 doi: 10.1016/j.ress.2018.05.014
    [3] S. L. N. Dhulipala, H. V. Burton, H. Baroud, A Markov framework for generalized post-event systems recovery modeling: from single to multihazards, Struct. Saf., 91 (2021), 102091. https://doi.org/10.1016/j.strusafe.2021.102091 doi: 10.1016/j.strusafe.2021.102091
    [4] N. Dehghani, E. Fereshtehnejad, A. Shafieezadeh, A Markovian approach to infrastructure life-cycle analysis: modeling the interplay of hazard effects and recovery, Earthquake Eng. Struct. Dyn., 50 (2021), 736–755. https://doi.org/10.1002/eqe.3359 doi: 10.1002/eqe.3359
    [5] X. Kong, J. Yang, Reliability analysis of composite insulators subject to multiple dependent competing failure processes with shock duration and shock damage self-recovery, Reliab. Eng. Syst. Saf., 204 (2020), 107166. https://doi.org/10.1016/j.ress.2020.107166 doi: 10.1016/j.ress.2020.107166
    [6] L. Wan, H. Chen, L. Ouyang, D. Zhang, Reliability modeling and analysis of multi-state dynamic degradation for complex equipment system of compliant mechanism, Syst. Eng. Theory Pract., 38 (2018), 2690–2702. https://doi.org/10.12011/1000-6788(2018)10-2690-13 doi: 10.12011/1000-6788(2018)10-2690-13
    [7] J. Wang, G. Bai, Z. Li, M. J. Zuo, A general discrete degradation model with fatal shocks and age- and state-dependent nonfatal shocks, Reliab. Eng. Syst. Saf., 193 (2020), 106648. https://doi.org/10.1016/j.ress.2019.106648 doi: 10.1016/j.ress.2019.106648
    [8] J. Wang, Z. Li, G. Bai, M. J. Zuo, An improved model for dependent competing risks considering continuous degradation and random shocks, Reliab. Eng. Syst. Saf., 193 (2020), 106641. https://doi.org/10.1016/j.ress.2019.106641 doi: 10.1016/j.ress.2019.106641
    [9] X. Wang, R. Ning, X. Zhao, C. Wu, Reliability assessments for two types of balanced systems with multi-state protective devices, Reliab. Eng. Syst. Saf., 229 (2023), 108852. https://doi.org/10.1016/j.ress.2022.108852 doi: 10.1016/j.ress.2022.108852
    [10] W. Dong, S. Liu, S. J. Bae, Y. Cao, Reliability modelling for multi-component systems subject to stochastic deterioration and generalized cumulative shock damages, Reliab. Eng. Syst. Saf., 205 (2020), 107260. https://doi.org/10.1016/j.ress.2020.107260 doi: 10.1016/j.ress.2020.107260
    [11] J. Zhang, T. Liu, J. Qiao, Solving a reliability-performance balancing problem for control systems with degrading actuators under model predictive control framework, J. Franklin Inst., 359 (2022), 4260–4287. https://doi.org/10.1016/j.jfranklin.2022.04.007 doi: 10.1016/j.jfranklin.2022.04.007
    [12] X. Zhao, S. Wang, X. Wang, Y. Fan, Multi-state balanced systems in a shock environment, Reliab. Eng. Syst. Saf., 193 (2020), 106592. https://doi.org/10.1016/j.ress.2019.106592 doi: 10.1016/j.ress.2019.106592
    [13] X. Wang, R. Ning, X. Zhao, J. Zhou, Reliability analyses of k-out-of-n: F capability-balanced systems in a multi-source shock environment, Reliab. Eng. Syst. Saf., 227 (2022), 108733. https://doi.org/10.1016/j.ress.2022.108733 doi: 10.1016/j.ress.2022.108733
    [14] S. Ranjkesh, A. Hamadani, S. Mahmoodi, A new cumulative shock model with damage and inter-arrival time dependency, Reliab. Eng. Syst. Saf., 192 (2019), 106047. https://doi.org/10.1016/j.ress.2018.01.006 doi: 10.1016/j.ress.2018.01.006
    [15] S. Anwar, S. Lone, A. Khan, S. Almutlak, Stress-strength reliability estimation for the inverted exponentiated Rayleigh distribution under unified progressive hybrid censoring with application, Electron. Res. Arch., 31 (2023), 4011–4033. https://doi.org/10.3934/era.2023204 doi: 10.3934/era.2023204
    [16] Y. Song, X. Wang, Reliability analysis of the multi-state k-out-of-n: F systems with multiple operation mechanisms, Mathematics, 10 (2022), 23. https://doi.org/10.3390/math10234615 doi: 10.3390/math10234615
    [17] M. Amirioun, F. Aminifar, H. Lesani, M. Shahidehpour, Metrics and quantitative framework for assessing microgrid resilience against windstorms, Int. J. Electr. Power Energy Syst., 104 (2019), 716–723. https://doi.org/10.1016/j.ijepes.2018.07.025 doi: 10.1016/j.ijepes.2018.07.025
    [18] Z. Zeng, Y. Fan, Q. Zhai, S. Du, A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events, Reliab. Eng. Syst. Saf., 209 (2021), 107443. https://doi.org/10.1016/j.ress.2021.107443 doi: 10.1016/j.ress.2021.107443
    [19] L. Liu, H. Wu, J. Wang, T. Yang, Research on the evaluation of the resilience of subway station projects to waterlogging disasters based on the projection pursuit model, Math. Biosci. Eng., 17 (2020), 7302–7331. https://doi.org/10.3934/mbe.2020374 doi: 10.3934/mbe.2020374
    [20] S. L. N. Dhulipala, M. M. Flint, Series of semi-Markov processes to model infrastructure resilience under multihazards, Reliab. Eng. Syst. Saf., 193 (2020), 106659. https://doi.org/10.1016/j.ress.2019.106659 doi: 10.1016/j.ress.2019.106659
    [21] J. Tang, L. Xu, C. Luo, T. S. Adam Ng, Multi-disruption resilience assessment of rail transit systems with optimized commuter flows, Reliab. Eng. Syst. Saf., 214 (2021), 107715. https://doi.org/doi:10.1016/j.ress.2021.107715 doi: 10.1016/j.ress.2021.107715
    [22] G. Levitin, M. Finkelstein, Y. Dai, Heterogeneous standby systems with shocks-driven preventive replacements, Eur. J. Oper. Res., 266 (2018), 1189–1197. https://doi.org/10.1016/j.ejor.2017.11.002 doi: 10.1016/j.ejor.2017.11.002
    [23] H. Dui, M. Liu, J. Song, S. Wu, Importance measure-based resilience management: review, methodology and perspectives on maintenance, Reliab. Eng. Syst. Saf., 237 (2023), 109383. https://doi.org/10.1016/j.ress.2023.109383
    [24] L. Chen, C. Cheng, H. Dui, L. Xing, Maintenance cost-based importance analysis under different maintenance strategies, Reliab. Eng. Syst. Saf., 222 (2022), 108435. https://doi.org/10.1016/j.ress.2022.108435 doi: 10.1016/j.ress.2022.108435
    [25] K. Andrzejczak, M. Mlynczak, J. Selech, Poisson-distributed failures in the predicting of the cost of corrective maintenance, Eksploatacja i Niezawodnosc–Maint. Reliab., 20 (2018), 602–609. https://doi.org/10.17531/ein.2018.4.11 doi: 10.17531/ein.2018.4.11
    [26] H. Dui, Z. Xu, L. Chen, L. Xing, B. Liu, Data-driven maintenance priority and resilience evaluation of performance loss in a main coolant system, Mathematics, 10 (2022), 563. https://doi.org/10.3390/math10040563 doi: 10.3390/math10040563
    [27] F. Santos, A. Teixeira, C. Soares, Maintenance planning of an offshore wind turbine using stochastic petri nets with predicates, J. Offshore Mech. Arct. Eng., 140 (2018), 021904. https://doi.org/10.1115/1.4038934 doi: 10.1115/1.4038934
    [28] J. Ren, S. Qu, L. Wang, L. Ma, T. Lu, Aircraft scheduling optimization model for on-ramp of corridors-in-the-sky, Electron. Res. Arch., 31 (2023), 3625–3648. https://doi.org/10.3934/era.2023184 doi: 10.3934/era.2023184
    [29] H. Dui, X. Wei, L. Xing, A new multi-criteria importance measure and its applications to risk reduction and safety enhancement, Reliab. Eng. Syst. Saf., 235 (2023), 109275. https://doi.org/10.1016/j.ress.2023.109275 doi: 10.1016/j.ress.2023.109275
    [30] J. Chang, X. Yin, C. Ma, D. Zhao, Y. Sun, Estimation of the time cost with pinning control for stochastic complex networks, Electron. Res. Arch., 30 (2022), 3509–3526. https://doi.org/10.3934/era.2022179 doi: 10.3934/era.2022179
    [31] A. Fawaz, R. Berthier, W. Sanders, A response cost model for advanced metering infrastructures, IEEE Trans. Smart Grid, 7 (2016), 543–553. https://doi.org/10.1109/tsg.2015.2418736 doi: 10.1109/tsg.2015.2418736
    [32] R. Yan, Y. Yang, Y. Du, Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation, Electron. Res. Arch., 31 (2023), 103–122. https://doi.org/10.3934/era.2023006 doi: 10.3934/era.2023006
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