Reliability – Event interval probability (EIP)

EIP is a newly developed data analysis method for detecting early change in system event interval data, such as failure times in engineered systems. The method draws upon statistics, probability and reliability theory and Monte Carlo simulation. The use of a null hypothesis allows probability values (p-values) to be calculated using the Poisson distribution, as well as Laplace and/or Crow-AMSAA. Analysis conducted contemporaneously with the event produces p-values that are statistical strength of evidence for rejection of the null hypothesis that event intervals are only random variation from expectation. Upon null rejection, investigation into cause of reliability degradation is triggered to avoid future failures that otherwise are to be expected. See technical papers and videos for applications demonstrating decision-making ability with only one failure or precursor event, as well as probabilistic risk assessment of uncorrected continued operation with only one data point – one failure. Automated data analysis is appropriate for large analysis volumes and/or results are needed in nearly real time. Consulting in automating the process is provided as well as training in the method and application of one dataset at a time analysis tools.

Availability/Capacity – PMF Series

Conventionally, availability is treated as a system state with datasets consisting of time to failure (TTF) and time to restore (TTR). Division of availability into the two components of reliability and maintainability is necessary for conventional analysis, including computer simulation, and has been the case for so long that it seems natural and appropriate. By introducing a new numbering system, PMF Series, the artificial separation of failure data into TTF and TTR is avoided and allows availability to be additionally defined as a quantity. This new numbering system for representing availability datasets is impractical for any use other than computer processing to generate a dense family of probability distributions for accumulated availability/capacity over any time interval. This family of probability distributions is accessible through a user interface – a Microsoft Excel workbook. With the user interface, system availability/capacity distributions and probabilistic risk assessments are efficiently presented and include consideration of current inventory, storage capacity and future shipping (demand) schedule. Consulting consists of preparing the user interface tool for client data and integrating the tool and analysis data into client decision-making. Training is on data analysis methodology and integration into client decision process. See technical papers for descriptions of this technology.