Reliability evaluation of safety-critical equipment under imperfect maintenance strategy

被引：0
|
作者：
Qu C. [1 ]
Wang H. [1 ]
Jiang W. [2 ]
Sun H. [1 ]
Zhang J. [3 ]
机构：
[1] Department of Safety Science and Engineering, School of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao
[2] Sinopec Research Institute of Safety Engineering, Qingdao
[3] CNOOC Safety & Technology Service Co. Ltd., Tianjin
来源：
Huagong Xuebao/CIESC Journal | 2021年 / 72卷 / 04期
关键词：
Imperfect repair; Instrumentation; Monte Carlo simulation; Parameter estimation; Safety;
D O I：
10.11949/0438-1157.20201144
中图分类号：
学科分类号：
摘要：
To realize the reliability evaluation of safety-critical equipment under incomplete maintenance conditions, the traditional reliability evaluation model does not consider the data difference between multiple fault samples and the complex problem of solving model parameters. This paper proposes a hybrid Kijima Ⅰ Virtual service age model. Firstly, the cumulative failure intensity function is used to describe the failure trend of the system on the time diagram. The appropriate reliability evaluation model is selected based on the AIC and BIC information criteria, and the non-linear constraint programming method is used to transform the estimated value of the distributed parameter under imperfect repair. Then, for the multi-category sample failure data caused by different reasons, the hybrid Kijima Ⅰ model is established by considering the difference between failure data. The case analysis of the ship unloading system of an LNG receiving station shows that this model is more effective than the commonly used mixed sample distribution model in actual reliability evaluation. At the same time this helps to achieve a balance between differentiated maintenance and high equipment availability. © 2021, Editorial Board of CIESC Journal. All right reserved.
引用
收藏
页码：2328 / 2336
页数：8
相关论文
共 36 条
  • [1] Ren L N, Rui Z Y, Liu J, Et al., Whole lifecycle reliability assessment of multiple NC machine tools with minimal repair, Journal of Shanghai Jiao Tong University, 49, 1, pp. 19-23, (2015)
  • [2] Wang Z M, Yang J G., Bayesian reliability analysis for NC machine tools with minimal repair, Computer Integrated Manufacturing Systems, 20, 9, pp. 2215-2220, (2014)
  • [3] Wang H, Zhao G C, Song Y N, Et al., Reliability evaluation method of hydraulic support based on improved Weibull distribution, China Safety Science Journal, 28, 5, pp. 99-104, (2018)
  • [4] Peng X K, Wang H Q, Liu J L, Et al., Maximum likelihood estimation of petrochemical safety equipment lifetime parameters, China Safety Science Journal, 29, 2, pp. 94-99, (2019)
  • [5] Wang H, Wang H Q, Liu F., Research on inference and forecasting technology for petrochemical equipment failure based on Bayesian method, Journal of Safety and Environment, 14, 6, pp. 5-7, (2014)
  • [6] Wang Z M, Yang J G., Bounded intensity process and its applications in reliability assessment of NC machine tools, Journal of Shanghai Jiao Tong University, 46, 10, pp. 1622-1626, (2012)
  • [7] Tanwar M, Rai R N, Bolia N., Imperfect repair modeling using Kijima type generalized renewal process, Reliability Engineering & System Safety, 124, pp. 24-31, (2014)
  • [8] Ren L N, Rui Z Y, Wang Z M., Imperfect repair continuous proportional intensity model for repairable systems and its application, Journal of Central South University (Science and Technology), 46, 7, pp. 2467-2473, (2015)
  • [9] Wang L, Hu H J, Wang Y Q, Et al., The availability model and parameters estimation method for the delay time model with imperfect maintenance at inspection, Applied Mathematical Modelling, 35, 6, pp. 2855-2863, (2011)
  • [10] Geng J, Zhou D, Lv C, Et al., A modeling approach for maintenance safety evaluation in a virtual maintenance environment, Computer-Aided Design, 45, 5, pp. 937-949, (2013)
1234