Bayesian analysis for the results of fatigue test using full-scale models to obtain the accurate failure probabilities of the Shinkansen vehicle axle

Akama, Makoto

doi:10.1016/s0951-8320(01)00129-6

Cited by 32 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is often used as the prior distribution in Bayesian inference because it is the conjugate prior of the multinomial distribution. Its probability density function is defined as the probabilities of n discrete variables are , i.e., , while follows a Dirichlet distribution , in which the PDF is defined as (5) where is the base measure of , and is a parameter showing how concentrated the probability would be around . When the value of is 1, the Dirichlet distribution is closer to a uniform distribution.…”

Section: B Dirichlet Process Mixture Model (Dpmm)mentioning

confidence: 99%

“…In order to estimate the component lifetime at a particular time period with limited real-life data and uncertain lifetime distribution, a nonparametric Bayesian approach at sub-system or component level is proposed here. Bayesian models have been employed in various railway system reliability studies [5][6][7], particularly in response to the uncertainty in the condition deterioration of the system or component through its life-cycle.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian nonparametric reliability analysis for a railway system at component level

Mokhtarian

Namzi-Rad

et al. 2013

2013 IEEE International Conference on Intelligent Rail Transportation Proceedings

View full text Add to dashboard Cite

Railway system is a typical large-scale complex system with interconnected sub-systems which contain numerous components. System reliability is retained through appropriate maintenance measures and cost-effective asset management requires accurate estimation of reliability at the lowest level. However, real-life reliability data at component level of a railway system is not always available in practice, let alone complete. The component lifetime distributions from the manufacturers are often obscured and complicated by the actual usage and working environments. Reliability analysis thus calls for a suitable methodology to estimate a component lifetime under the conditions of a lack of failure data and unknown and/or mixture lifetime distributions. This paper proposes a nonparametric Bayesian approach with a Dirichlet Process Mixture Model (DPMM) to facilitate reliability analysis in a railway system. Simulation results will be given to illustrate the effectiveness of the proposed approach in lifetime estimation.

show abstract

Section: B Dirichlet Process Mixture Model (Dpmm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian nonparametric reliability analysis for a railway system at component level

Mokhtarian

Namzi-Rad

et al. 2013

2013 IEEE International Conference on Intelligent Rail Transportation Proceedings

View full text Add to dashboard Cite

show abstract

“…• Validation and updating of numerical fatigue life simulations with sparse full-scale tests for train wheel axles [12].…”

Section: • Grid Integrationmentioning

confidence: 99%

Addressing failure rate uncertainties of marine energy converters

2012

View full text Add to dashboard Cite

The interest in marine renewable energy is strong, but has not led to significant commercial scale investment and deployment, yet. To attract investors and promote the development of a marine renewable industry a clear concept of project risk is paramount, in particular issues relating to device reliability are critical. In the public domain, reliability information is often scarce or inappropriate at this early stage of development, as little operational experience has been gained. Thus, reliability estimates are fraught with large uncertainties. This paper explores sources and magnitudes of failure rate uncertainty and demonstrates the effect on reliability estimates for a notional marine energy converter. If generic failure rate data forms the basis of a reliability assessment, reliability estimates are not robust and may significantly over-or underestimate system reliability. The Bayesian statistical framework provides a method to overcome this issue. Generic data can be updated with more specific information that could not be statistically incorporated otherwise. It is proposed that adopting such an approach at an early stage in an iterative process will lead to an improved rate of certainty.

show abstract

“…A fuzzy number is a subset, denoted byx, of the set of real numbers (denoted by ) and is characterized by the so called membership function x (·). Fuzzy numbers satisfy the following constraints [15]: 1]. This means that the membership function has to be a unimodal function with a maximum.…”

Section: Fuzzy Lifetime Datamentioning

confidence: 99%

“…Thus, it can be used even when there are only a few lifetime data points. Many research results on the classic Bayesian reliability analysis have been reported and many practical problems have been solved successfully with this method [1,28].…”

Section: Introductionmentioning

confidence: 99%