Time-dependent stress–strength reliability models based on phase type distribution

Jose, Joby K.; Drisya, M.

doi:10.1007/s00180-020-00991-3

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…Siju and Kumar 30 regarded the distribution of cycle time as an exponential distribution and the distribution of strength as a Weibull or exponential distribution, assuming that the stress of the system was fixed. Jose and Drisya 31 extend the previous research by considering stress as a variable and proposed a reliability model timedependent stress-strength reliability models subjected to random stresses at random cycles of time. Xavier and Jose 32 studied the problem of Bayesian estimation of stress-strength reliability and obtained an expression for the stress-strength reliability.…”

Section: Introductionmentioning

confidence: 90%

Reliability modeling for dependent competing failure processes considering random cycle times

Lyu,

Lu,

Xie

et al. 2023

Quality & Reliability Eng

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In this paper, a novel model is proposed for the reliability analysis of systems subjected to dependent competing failure processes at random cycle times. The model takes into account the effect of random cycle time on degradation rate and hard failure threshold. Random cycle time is the time required for the system to complete a run. With the continuous operation of the system in a period of cycle time, the degradation rate of the system is accelerated, and its resistance to random shocks is decreased, so the degradation rate and hard failure threshold are supposed to vary with the number of cycles. When the overall degradation exceeds the soft failure threshold, soft failure is triggered. When the shock magnitude is larger than the hard failure threshold, hard failure occurs. Combined with the stress‐strength interference model, the reliability function of the system is derived. Finally, a Micro‐Electro‐Mechanical System is used to verify the validity of the proposed model. The validity of the model is demonstrated by sensitivity analysis.

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Section: Introductionmentioning

confidence: 90%

Reliability modeling for dependent competing failure processes considering random cycle times

Lyu,

Lu,

Xie

et al. 2023

Quality & Reliability Eng

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“…Thereafter, the problem of estimating R has been discussed by a great number of researchers. Of the recent efforts pertaining to stress-strength models, to name a few, are Al-Mutairi, Ghitany, and Kundu (2013), Genc (2013), Singh, Singh, and Sharma (2014), Rezaei, Noughabi, and Nadarajah (2015), Akgül and Şenoglu (2017), Mahdizadeh and Zamanzade (2018), Abravesh, Ganji, and Mostafaiy (2019), Jose and Drisya (2020), Sadeghpour, Nezakati, and Salehi (2021) and Biswas, Chakraborty, and Mukherjee (2021).…”

Section: Introductionmentioning

confidence: 99%

Multicomponent Stress-strength Reliability with Exponentiated Teissier Distribution

Pasha-Zanoosi

Pourdarvish²,

Asgharzadeh

2022

AJS

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This article deals with the problem of reliability in a multicomponent stress-strength (MSS) model when both stress and strength variables are from exponentiated Teissier (ET) distributions. The reliability of the system is determined using both classical and Bayesian methods, based on two scenarios where the common scale parameter is unknown or known. In the first scenario, where the common scale parameter is unknown, the maximum likelihood estimation (MLE) and the approximate Bayes estimation are derived. In the second scenario, where the scale parameter is known, the MLE, the uniformly minimum variance unbiased estimator (UMVUE) and the exact Bayes estimation are obtained. In the both scenarios, the asymptotic confidence interval and the highest probability density credible interval are established. Furthermore, two other asymptotic confidence intervals are computed based on the Logit and Arcsin transformations. Monte Carlo simulations are implemented to compare the different proposed methods. Finally, one real example is presented in support of suggested procedures.

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Estimation of $$P[Y<Z]$$ under Geometric-Lindley model

2023

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Time-dependent stress–strength reliability models based on phase type distribution

Cited by 8 publications

References 37 publications

Reliability modeling for dependent competing failure processes considering random cycle times

Reliability modeling for dependent competing failure processes considering random cycle times

Multicomponent Stress-strength Reliability with Exponentiated Teissier Distribution

Estimation of $$P[Y<Z]$$ under Geometric-Lindley model

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