RESEARCH PAPER
Reliability Assessment of Competitive Failure Systems Based on Three Parameter Weibull Distribution and Wiener Process
1
Xi'an University of Technology, China
2
University of Science and Technology Beijing, China
Submission date: 2024-08-14
Final revision date: 2024-10-08
Acceptance date: 2024-12-20
Online publication date: 2024-12-26
Publication date: 2024-12-26
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):199426
HIGHLIGHTS
- The hard failure process is constructed by the three-parameter Weibull distribution.
- The unknown parameters of the reliability model are estimated by the MCMC-MH method.
- A nonlinear function is proposed to design the correlation of different failure processes.
KEYWORDS
Reliability AssessmentDegradation FailureCompeting FailureWiener ProcessThree-Parameter Weibull Distribution
TOPICS
ABSTRACT
Considering the competitive failure that exists in the operation of industrial systems, including degradation failure and sudden failure, this paper presents a reliability assessment method based on the three-parameter Weibull distribution and the Wiener process. Nonlinear functions are proposed to establish the relationship between the different failure processes, and the reliability model is derived. The Metropolis-Hastings (MH) sampling algorithm of the Monte Carlo Markov Chain (MCMC) method is employed to estimate the parameters in this study. The reliability assessment results are obtained by the real degradation samples. The results show that the reliability model incorporating the three-parameter Weibull distribution produces more comprehensive and dependable results. Furthermore, MH sampling can solve the issues of complex likelihood functions that cannot directly obtain parameter evaluation results. Additionally, this paper analyzes the sensitivity of the model parameters, thereby offering theoretical support for enhancing the safe operation of the system.
ACKNOWLEDGEMENTS
This work was supported by the National Science Foundation of China (No. 62403376, No. U2034209, No. 62120106011). The Research startup foundation of Xi’an University of Technology (No. 451124001)
REFERENCES (40)
1.
Jiawen Hu, Qiuzhuang Sun, Zhisheng Ye, Condition-Based maintenance planning for systems subject to dependent soft and hard failures[J], IEEE Transactions on Reliability, vol. 70, no. 4, pp. 1468-1480, 2021. https://doi.org/10.1109/TR.202....
2.
Anqi Shangguan, Nan Feng, Lingxia Mu, Rong Fei, Xinhong Hei, Guo Xie[J], Quality and Reliability Engineering International, vol. 39, no. 7, pp. 2851-2868, 2023. https://doi.org/10.1002/qre.33....
3.
Anqi Shangguan, Guo Xie, Rong Fei, Lingxia Mu, Xinhong Hei, Train wheel degradation generation and prediction based on the time series generation adversarial network[J], Reliability Engineering & System Safety, vol.229. 2022.
5.
Lina Bian, Guanjun Wang, Peng Liu, Reliability analysis for k-out-of-n(G) systems subject to dependent competing failure processes, Computers & Industrial Engineering, Volume 177, 2023, 109084, 2023.
6.
Jianing Man, Qiang Zhou,Prediction of hard failures with stochastic degradation signals using Wiener process and proportional hazards model, Computers & Industrial Engineering, Volume 125, Pages 480-489, 2018. https://doi.org/10.1016/j.cie.....
7.
Miaoxin Chang, Xianzhen Huang, Frank P.A. Coolen, Tahani Coolen-Maturi, Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels[J], Reliability Engineering & System Safety, vol.216. 108007, 2021. https://doi.org/10.1016/j.ress....
8.
Riccardo Amirante, Elia Distaso, Paolo Tamburrano, sliding spool design for reducing the actuation forces in direct operated proportional directional valves: Experimental validation[J], Energy Conversion and Management, Vol. 119, pp. 399-410, 2016. https://doi.org/10.1016/j.enco....
9.
YanHui Lin, YanFu Li, Enrico Zio, Reliability assessment of systems subject to dependent degradation processes and random shocks[J], IIE Transactions, vol. 48, no. 11, pp. 1072–1085, 2016. https://doi.org/10.1080/074081....
10.
Chuanxi Jin, Yan Ran, Zhichao Wang, Guangquan Huang, Liming Xiao, Genbao Zhang, Reliability analysis of gear rotation meta-action unit based on Weibull and inverse Gaussian competing failure process, Engineering Failure Analysis, vol 117, 104953, 2020. https://doi.org/10.1016/j.engf....
11.
Guangze Pan, Guangkuo Guo, Dan Li, Yaqiu Li, Qian Li, Wenwei Liu, A reliability analysis method based on the mixed correlated competition model considering multi-performance degradation and sudden failures, Engineering Failure Analysis, vol 146, 107126, 2023. https://doi.org/10.1016/j.engf....
12.
Gang Pan, Chaoxuan Shang, Yuying Liang, Jinyan Cai, Yafeng Meng, Reliability evaluatioin of radar power amplifier system in case of related competing failures[J], Acta Electronica Sinica, vol. 45, no. 4, pp.805-812, 2017. (in Chinese).
13.
Zhiyuan Yang, Jianmin Zhao, Zhonghua Cheng, Liying Li, Chi Kuo, Reliability model of competing failure system with dependent degradation[J], Acta Armamentarii, vol. 41, no. 7, pp. 1424-1433, 2020. (in Chinese).
14.
Xingang Wang, Lin Li, Miaoxin Chang, Kaizhong Han, Reliability modeling for competing failure processes with shifting failure thresholds under severe product working conditions[J], Applied Mathematical Modelling, Vol. 89, pp. 1747-1763, 2021. https://doi.org/10.1016/j.apm.....
15.
Anqi Shangguan, Guo Xie, Lingxia Mu, Rong Fei, Xinhong Hei, Reliability modeling: combining self-healing characteristics and competing failure process[J], Quality Technology & Quantitative Management, vol. 21, no.3, pp. 363-385, 2023. https://doi.org/10.1080/168437....
16.
Hongda Gao, Lirong Cui, Qingan Qiu, Reliability modeling for degradation-shock dependence systems with multiple species of shocks[J], Reliability Engineering & System Safety, vol. 185, pp. 133-143, 2019. https://doi.org/10.1016/j.ress....
17.
Yankai Qin, Xiaohong Zhang, Jianchao Zeng, Guannan Shi, Bin Wu, Reliability Analysis of Mining Machinery Pick Subject to Competing Failure Processes with Continuous Shock and Changing Rate Degradation[J], IEEE Transactions on Reliability, vol. 72, no.2, pp.795-807, 2023. https://doi.org/10.1109/TR.202....
18.
Hao Lyu, Hongchen Qu, Zaiyou Yang, Li Ma, Bing Lu, Michael Pecht, Reliability analysis of dependent competing failure processes with time-varying δ shock model[J], Reliability Engineering & System Safety, vol. 229, 108876, 2023.
20.
Chunfang Zhang, Liang Wang, Xuchao Bai, Jianan Huang, Bayesian reliability analysis for Copula based step-stress partially accelerated dependent competing risks model[J], Reliability Engineering & System Safety, vol. 227, 108718, 2022.
22.
Wenjie Dong, Sifeng Liu, Suk Joo Bae, Yingsai Cao, Reliability modelling for multi-component systems subject to stochastic deterioration and generalized cumulative shock damages[J], Reliability Engineering & System Safety, vol. 205, 107260, 2021.
24.
Jia Wang, Zhigang Li, Guanghan Bai, Ming J. Zuo, An improved model for dependent competing risks considering continuous degradation and random shocks, Reliability Engineering & System Safety, vol. 193, 106641, 2020. https://doi.org/10.1016/j.ress....
25.
Hao Lyu, Shuai Wang, Zaiyou Yang, Hongchen Qu, Li Ma, Reliability modeling for multi-component system subject to dependent competing failure processes with phase-type distribution considering multiple shock sources, Quality Engineering, vol. 35, no. 1, pp. 95-109, 2023. https://doi.org/10.1080/089821....
26.
Mengfei Fan, Zhiguo Zeng, Enrico Zio, Rui Kang, Modeling dependent competing failure processes with degradation-shock dependence[J], Reliability Engineering & System Safety, vol. 165, pp. 422–430, 2017. https://doi.org/10.1016/j.ress....
27.
Haiyang Che, Shengkui Zeng, Jianbin Guo, Yao Wang, Reliability modeling for dependent competing failure processes with mutually dependent degradation process and shock process[J], Reliability Engineering & System Safety, vol. 180, pp. 168–178, 2018.
29.
Li Yang, Xiaobing Ma, Rui Peng, Qingqing Zhai, Yu Zhao, A preventive maintenance policy based on dependent two-stage deterioration and external shocks[J], Reliability Engineering & System Safety, vol. 160, pp. 201–211, 2017. https://doi.org/10.1016/j.ress....
30.
Xingang Wang, Xinyao Zhang, Lujie Yang, Ruimin Ma, Tool reliability analysis for wear degradation data under competitive failure conditions[J], China Mechanical Engineering, vol. 31, no. 14, pp. 1672-1677, 2020. (in Chinese).
31.
Nooshin Yousefi, David W. Coit, Xiaoyan Zhu, Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes, Computers & Industrial Engineering, Volume 143, 2020, 106398,.
33.
Ancheng Xue, Lin Luo, Qi Jing, Junhao Wang, Xuankun Song, Ying Liu, Jun Li, ShaoFeng Huang Tianshu Bi, Research on aging failure rate estimation of protective relay based on three-parameter Weibull distribution[J], Power System Protection and Control, vol. 42, no. 24, pp. 72-78, 2014. (in Chinese).
34.
Meng Xu, Huachao Mao, q-Weibull Distributions: Perspectives and Applications in Reliability Engineering[J], IEEE Transactions on Reliability, https://doi.org/10.1109/TR.202....
35.
Mohamed Kayid, Salah Djemili, Reliability Analysis of the Inverse Modified Weibull Model with Applications[J], Mathematical Problems in Engineering, vol. 2022, 4005896, 2022. https://doi.org/10.1155/2022/4....
36.
Fucheng Han, Xin Li, Shengwenjun Qi, Wenhua Wang, Wei Shi, Reliability analysis of wind turbine subassemblies based on the 3-P Weibull model via an ergodic artificial bee colony algorithm[J], Probabilistic Engineering Mechanics, vol. 73, 103476, 2023.
38.
Jesus M. Barraza-Contreras, Manuel R. R. Pina-Monarrez, Roberto C. C. orres-Villasenor, Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution[J], Applied Sciences-Basel, vol. 13, no.7,4403, 2023. https://doi.org/10.3390/app130....
39.
Cox D R, Miller H D, The theory of stochastic processes[M]. London: chapman and Hall, 1965.
40.
Meeker W Q, Escobar L A, Statistical methods for reliability data[M], John Wiley & Sons, New York, 1998.
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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