Special Cases in Failure Analysis: Research on the Optimal Confidence Limit Evaluation by Zero-Failure Data

Li, Haiyang; Zheng, Zeyu; Yang, Zihao; Xu, Chen

doi:10.1007/s11668-020-00966-y

Cited by 2 publications

(1 citation statement)

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“…Studies on the reliability problem without failure data have attracted increasing interest from the academic and engineering circles. Given that the service time of bearings follows the Weibull distribution [17], Li et al [18] proposed a reliability analysis method without failure data, which assesses the reliability of rolling bearings by expanding the optimal confidence interval. Meanwhile, the influences of the bearing grouping mode and the shape parameters of the model on reliability assessment results were analyzed.…”

Section: Literature Reviewmentioning

confidence: 99%

Reliability Assessment Model and Simulation of Journal Bearing of Railway Freight Cars Based on Bayesian Method under Small Sample Sizes

Yang

Len

Wang

et al. 2022

Journal of Advanced Transportation

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Generally, the box bearing of railway freight cars has no bearing sample failure data at the end of the time-terminated reliability test. However, it is expensive and has high service reliability requirements. Given a small sample size and zero-failure data, the traditional failure probability calculation formula based on a large sample size and the reliability modeling technique cannot easily assess the reliability of rolling bearings accurately. Considering the applicability of the bearing of railway freight cars, this study integrated the prior information of samples and the simulation test information according to Bayes statistical theory, deduced the mathematical model of cumulative failure probability under failure-free data, calculated the distribution parameters using the least square method, and established the reliability estimation model of rolling bearings on the basis of Weibull distribution. The failure-free simulation data of rolling bearings were produced according to the Monte Carlo simulation, and the reliability of the journal bearing of railway freight cars was simulated and assessed by three methods. Simulation results demonstrate that the proposed reliable Bayes multilayer estimation method could not only meet the design requirements of the ISO 281 rolling bearing standards on that basis of the failure-free data and small sample size of the time-terminated simulation, but also assess the reliability of the rolling bearing of railway freight cars.

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Section: Literature Reviewmentioning

confidence: 99%