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Stochastic Simulator for Reliability Modeling of Repairable Systems
Description
A toolkit intended for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
It is recommended for use with RExcel for data input, holding of the model
scripts, and ad hoc analysis of reliability parameters.
Introduction
stosim provides functions for creating reliability models using observed
data reduced to probability distributions for failure and repair mechanisms
on related operations in order to infer expected performance of new systems
or alteration of existing systems.
Models can be assembled from small sub-systems and accumulated to describe
an entire production plant or refinery. Stochastic modeling provides an ideal
means for study of the performance of product inventory storage and parallel
operations as reliability enhancement features. Time dependent issues such as
seasonal variation, and equipment degradation can be accurately assessed for
impact on ultimate production capability. Contractual conditions such as
bonus/penalty clauses can be evaluated with realitic statistical projections.
Author(s)
Jacob T. Ormerod
Maintainer: Jacob T. Ormerod <jake@openreliability.org>
References
Jones, O.D., R. Maillardet, and A.P. Robinson (2009) An Introduction
to Scientific Programming and Simulation, Using R. Chapman And Hall/CRC
Robert, Christian P., G. Casella (2010) Introducing Monte Carlo Methods with R.
Springer
Taylor HM, Karlin S (1998) An Introduction to Stochastic Modeling, 3rd Edition,
Acadmic Press.
Silkworth, David J. (1998) "Confidence Curves: A Reliability Modelling Technique for
the Practical Application of Process Unit and Subsystem Failure Data". American
Institute of Chemical Engineers
Tobias, Paul A., D.C. Trinidade (1986)Applied Reliability. Van Nostand Reinhold
stosim documentation built on Aug. 28, 2023, 5:08 p.m.
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| stosim-package | R Documentation |
Stochastic Simulator for Reliability Modeling of Repairable Systems
Description
A toolkit intended for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
It is recommended for use with RExcel for data input, holding of the model scripts, and ad hoc analysis of reliability parameters.
Introduction
stosim provides functions for creating reliability models using observed data reduced to probability distributions for failure and repair mechanisms on related operations in order to infer expected performance of new systems or alteration of existing systems. Models can be assembled from small sub-systems and accumulated to describe an entire production plant or refinery. Stochastic modeling provides an ideal means for study of the performance of product inventory storage and parallel operations as reliability enhancement features. Time dependent issues such as seasonal variation, and equipment degradation can be accurately assessed for impact on ultimate production capability. Contractual conditions such as bonus/penalty clauses can be evaluated with realitic statistical projections.
Author(s)
Jacob T. Ormerod
Maintainer: Jacob T. Ormerod <jake@openreliability.org>
References
Jones, O.D., R. Maillardet, and A.P. Robinson (2009) An Introduction to Scientific Programming and Simulation, Using R. Chapman And Hall/CRC
Robert, Christian P., G. Casella (2010) Introducing Monte Carlo Methods with R. Springer
Taylor HM, Karlin S (1998) An Introduction to Stochastic Modeling, 3rd Edition, Acadmic Press.
Silkworth, David J. (1998) "Confidence Curves: A Reliability Modelling Technique for the Practical Application of Process Unit and Subsystem Failure Data". American Institute of Chemical Engineers
Tobias, Paul A., D.C. Trinidade (1986)Applied Reliability. Van Nostand Reinhold
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