Mechanical Engineering Science

Reliability analysis of small failure probability based on subset simulation method

WANGHongtao (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China), SolomonKetema Mikiyas (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China), AyicheluhemTirfe Natnael (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China)

Abstract


In the engineering, to ensure the quality and safety, it is necessary to carry out reliability analysis on it. When conducting reliability analysis in engineering, a large number of small failure probability problems will be encountered. For such problems, the traditional Monte Carlo method needs a lot of samples, and the calculation efficiency is extremely low, while the subset simulation method can efficiently estimate the reliability index of the small failure probability problem with little samples. Therefore, this paper takes the application of the subset simulation method in the reliability analysis of the small failure probability structure as the object, constructs the reliability analysis method of the single failure mode of the system, and applies the method to a mathematical example and a single-story gate. Through the rigid frame example, it can be seen that this method is beneficial to improve the calculation efficiency and accuracy.

Keywords


subset simulation; small failure probability; failure mode; reliability analysis

Full Text:

PDF

References


Modarres M, Kaminskiy M P, Krivtsov V, 2016. Reliability engineering and risk analysis: a practical guide. CRC press.

Huang Z L, Jiang C, Zhang Z, et al., 2019. Evidence-theory-based reliability design optimization with parametric correlations. Structural and Multidisciplinary Optimization, 60(2): 565-580.

Huang Z L, Jiang C, Zhang Z, et al., 2017. A decoupling approach for evidence-theory-based reliability design optimization. Structural and Multidisciplinary Optimization, 56(3): 647-661.

Sahu K, K Srivastava R, 2020. Needs and importance of reliability prediction: An industrial perspective. Information Sciences Letters, 9(1): 5.

BahooToroody A, De Carlo F, Paltrinieri N, et al., 2020. Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation. Reliability Engineering & System Safety, 201: 106966.

Cheng Y, Zhu H, Hu K, Wu J, et al., 2019. Reliability prediction of machinery with multiple degradation characteristics using double-Wiener process and Monte Carlo algorithm. Mechanical Systems and Signal Processing, 134:106333.

Aslam M, 2019. A new failure-censored reliability test using neutrosophic statistical interval method. International Journal of Fuzzy Systems, 21(4):1214-1220.

Fan C L, Song Y, Lei L, et al., 2018. Evidence reasoning for temporal uncertain information based on relative reliability evaluation. Expert Systems with Applications, 113:264-276. [9] Meng D, Yang S, Zhang Y, et al., 2019. Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model. Fatigue & Fracture of Engineering Materials & Structures, 42(6) 1219-1227.

Liu Y, Zuo M J, Li Y F, et al., 2015. Dynamic reliability assessment for multi-state systems utilizing system-level inspection data. IEEE Transactions on Reliability, 64(4):1287-1299.

Lu L, Anderson-Cook C M, 2017. Choosing a reliability inspection plan for interval censored data. Quality Engineering, 29(3):512-535.

Grishko A, Adnreev P, Goryachev N, et al., 2018. Reliability control of complex systems at different stages of their life cycle. In 2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (pp. 220-223).

De Almeida A T, Ferreira R J P, Cavalcante, C A V, 2015. A review of the use of multicriteria and multi-objective models in maintenance and reliability. IMA Journal of Management Mathematics, 26(3):249-271.

De Almeida A T, Cavalcante C A V, Alencar M H, et al., 2015. Multicriteria and multiobjective models for risk, reliability and maintenance decision analysis (Vol. 231). New york: Springer International Publishing.

Abdullah L, Singh S S K, Azman A H, et al, 2019. Fatigue life-based reliability assessment of a heavy vehicle leaf spring. International Journal of Structural Integrity, 10(5):726-736.

Liu X, Zhang Y, Xie S, et al., 2021. Fatigue failure analysis of express freight sliding side covered wagon based on the rigid-flexibility model. International Journal of Structural Integrity, 12(1):98-108.

Li S, Liu X, Wang X, et al., 2020, Fatigue life prediction for automobile stabilizer bar. International Journal of Structural Integrity, 11(2):303-323.

Nahal M, Khelif R, 2021. A finite element model for estimating time-dependent reliability of a corroded pipeline elbow. International Journal of Structural Integrity, 12(2):306-321.

Abd Rahim A A, Abdullah S, Singh S S K, et al., 2019. Reliability assessment on automobile suspension system using wavelet analysis. International Journal of Structural Integrity, 10(5): 602-611.

Meng D, Wang H, Yang S, et al., 2022. Fault analysis of wind power rolling bearing based on EMD feature extraction. CMES-Computer Modeling in Engineering & Sciences, 130(1): 543-558.

Yang S, Wang J, Yang H, 2022. Evidence theory based uncertainty design optimization for planetary gearbox in wind turbine. Journal of Advances in Applied & Computational Mathematics, 9: 86-102.

Yang Y M, Yu H, Sun Z, 2017. Aircraft failure rate forecasting method based on Holt-Winters seasonal model. In 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), (4):520-524).

Yang Y, Zheng H, Zhang R, 2017. Prediction and analysis of aircraft failure rate based on SARIMA model. In 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) (pp. 567-571).

Wang B J, Li Q, Ren Z S, et al., 2020. Improving the fatigue reliability of metro vehicle bogie frame based on load spectrum. International Journal of Fatigue, 132:105389.

Kong Y S, Abdullah S, Schramm D, et al., 2019. Development of multiple linear regression-based models for fatigue life evaluation of automotive coil springs. Mechanical Systems and Signal Processing, 118:675-695.

Yun W, Lu Z, Jiang X, 2018. An efficient reliability analysis method combining adaptive Kriging and modified importance sampling for small failure probability. Structural and Multidisciplinary Optimization, 58(4):1383-1393.

Xiao N C, Zhan , Yuan K, 2020. A new reliability method for small failure probability problems by combining the adaptive importance sampling and surrogate models. Computer Methods in Applied Mechanics and Engineering, 372, 113336.

Zhang J, Xiao M, Gao L, et al., 2019. A combined projection-outline-based active learning Kriging and adaptive importance sampling method for hybrid reliability analysis with small failure probabilities. Computer Methods in Applied Mechanics and Engineering, 344:13-33.

Gong W, Juang C H, Martin J R, et al., 2016. New sampling method and procedures for estimating failure probability. Journal of Engineering Mechanics, 142(4):04015107.

Meng D, Yang S, Lin T, et al., 2022. RBMDO using gaussian mixture model-based second-order mean-value saddlepoint approximation. CMES-Computer Modeling in Engineering & Sciences, 132(2): 553-568.

Pan Q , Dias D, 2017. An efficient reliability method combining adaptive support vector machine and Monte Carlo simulation. Structural Safety, 67:85-95.

Hsu W C, Ching J, 2010. Evaluating small failure probabilities of multiple limit states by parallel subset simulation. Probabilistic Engineering Mechanics, 25(3):291-304.

Meng D, Yang S, de Jesus A M, Zhu S P, 2022. A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower. Renewable Energy. https://doi.org/10.1016/j.renene.2022.12.

Yang Z, Kan Y, Chen F, et al., 2015. Bayesian rel Chinese Journal of Mechanical Engineering, 28(6):1229-1239.

Yuan R, Tang M, Wang H, et al., 2019. A reliability analysis method of accelerated performance degradation based on bayesian strategy. IEEE Access, 7:169047-169054.

Meng D, Yang S, de Jesus A M, Zhu S P, 2022. A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower. Renewable Energy. https://doi.org/10.1016/j.renene.2022.12.

Li H S, Ma Y Z, Cao Z, 2015. A generalized Subset Simulation approach for estimating small failure probabilities of multiple stochastic responses. Computers & Structures, 153:239-251.

Huang X, Chen J, Zhu H, 2016. Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation. Structural Safety, 59:86-95.

Abdollahi A, Moghaddam M A, Monfared S A , et al., 2020. A refined subset simulation for the reliability analysis using the subset control variate. Structural Safety, 87:102002.

Au S K, Beck J L, 2001. Estimation of small failure probabilities in high dimensions by subset simulation. Probabilistic engineering mechanics, 16(4):263-277.

Xiao M, Zhang J, Gao L, et al., 2019. An efficient Kriging-based subset simulation method for hybrid reliability analysis under random and interval variables with small failure probability. Structural and Multidisciplinary Optimization, 59(6):2077-2092.

Qian H M, Li Y F, Huang H Z, 2021. Time-variant system reliability analysis method for a small failure probability problem. Reliability Engineering & System Safety, 205:107261.

Meng D, Yang S, He C, et al., 2022. Multidisciplinary design optimization of engineering systems under uncertainty: a review. International Journal of Structural Integrity, 13(4), 565-593.

Van Ravenzwaaij D, Cassey P, Brown S D, 2018. A simple introduction to Markov Chain Monte–Carlo sampling. Psychonomic bulletin & review, 25(1):143-154.

Jensen H A, Jerez D J, Valdebenito M, 2020. An adaptive scheme for reliability-based global design optimization: A Markov chain Monte Carlo approach. Mechanical Systems and Signal Processing, 143:106836.




DOI: https://doi.org/10.33142/mes.v4i1.7512

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Hongtao WANG, Ketema Mikiyas Solomon, Tirfe Natnael Ayicheluhem

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.