Reliability inferences of modulated power-law process #i

Muralidharan, K.

doi:10.1109/24.994901

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…The papers which discuss inferences based on Bayesian set up are due to Soland [28], Calabria and Pulcini [7], Sen [27], Pievatolo and Ruggeri [21], Guida and Pulcini [13] and references contained therein. introduced by Lakey and Rigdon [15] and studied thereafter by Black and Rigdon [6], Muralidharan [19,20] and Ryan [26].…”

Section: ____________________________________________________________mentioning

confidence: 99%

“…Baker [5], Jani et al [14] and Muralidharan [17] have proposed various tests for Weibull process. Recently Muralidharan [19] has studied reliability inferences of Weibull process under different realizations. Gaudoin et al [11] have proposed goodness-of-fit test for the Weibull process based on the Duane plot.…”

Section: ____________________________________________________________mentioning

confidence: 99%

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Future Reliability Estimation Based on Predictive Distribution in Power Law Process

Muralidharan

Shah

Dhandhukia

2008

Quality Technology & Quantitative Management

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Power law process or Weibull process is used to model reliability growth. The reliability development of such a system often takes place by testing a system until it fails, then making repairs and design changes and testing it again. This process continues until a desired level of reliability is achieved. At various times in the development process it becomes important to assess and predict system reliability from an available set of data on system failures. We made an attempt to estimate the future reliability based on a predictive distribution. We also discuss the point and interval estimations of the parameters as well as reliability based on full likelihood and predictive likelihood. A detailed simulation along with a few examples is presented to support the developments.

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Section: ____________________________________________________________mentioning

confidence: 99%

Section: ____________________________________________________________mentioning

confidence: 99%

Future Reliability Estimation Based on Predictive Distribution in Power Law Process

Muralidharan

Shah

Dhandhukia

2008

Quality Technology & Quantitative Management

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“…For some recent references, please see Refs. [4][5][6][7][8]. From a practical point of view, however, it is always preferred to apply the simplest method to address problems and to obtain useful practical results.…”

Section: Introductionmentioning

confidence: 98%

Reliability analysis of repairable systems using stochastic point processes

Tan

Jiang

Bai

2008

J. Shanghai Jiaotong Univ. (Sci.)

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In order to analyze the failure data from repairable systems, the homogeneous Poisson process (HPP) is usually used. In general, HPP cannot be applied to analyze the entire life cycle of a complex, repairable system because the rate of occurrence of failures (ROCOF) of the system changes over time rather than remains stable. However, from a practical point of view, it is always preferred to apply the simplest method to address problems and to obtain useful practical results. Therefore, we attempted to use the HPP model to analyze the failure data from real repairable systems. A graphic method and the Laplace test were also used in the analysis. Results of numerical applications show that the HPP model may be a useful tool for the entire life cycle of repairable systems.

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“…Though these authors call this stochastic process the modulated power law process (MPLP), it can be regarded as a complete parametric version based on the power law-type NHPP and a gamma renewal process. Muralidharan [33,34] and Muralidharan et al [35] examine the statistical test and reliability prediction for MPLP. Calabria and Pulcini [13,14] apply the Bayes approach to the parameter inference and testing for MPLP.…”

Section: Introductionmentioning

confidence: 99%

Another Look at Nonparametric Estimation for Trend Renewal Processes

Saito

2016

JORSJ

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A trend renewal process is characterized by a counting process and a renewal process which are mutually transformed with each other by a trend function, and plays a significant role to represent a sub-class of general repair models. In this paper we develop another nonparametric estimation method for trend renewal processes, where the form of failure rate function in the renewal process is unknown. It is regarded as a dual approach for the nonparametric monotone maximum likelihood estimator by Heggland and Lindqvist (2007) and complements their result under the assumption that the form of trend (intensity) function is unknown. We validate our nonparametric estimator through simulation experiments and apply to a field data analysis of a repairable system.

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Reliability inferences of modulated power-law process #i

Cited by 5 publications

References 8 publications

Future Reliability Estimation Based on Predictive Distribution in Power Law Process

Future Reliability Estimation Based on Predictive Distribution in Power Law Process

Reliability analysis of repairable systems using stochastic point processes

Another Look at Nonparametric Estimation for Trend Renewal Processes

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