Description Introduction Author(s) References
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.
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.
Jacob T. Ormerod
Maintainer: Jacob T. Ormerod <firstname.lastname@example.org>
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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