Reliability HotWire

Issue 98, April 2009

Tool Tips

* BlockSim and RENO appear to have overlapping capabilities. What are the core differences between BlockSim and RENO?

BlockSim uses logic diagrams in the form of Reliability Block Diagrams (RBDs) or fault trees. These logic diagrams describe the ways a system can fail or succeed. In BlockSim, blocks represent components, subsystems, assemblies, events, etc. and have a number of reliability and maintainability properties which determine the outcome of a simulation. How the blocks are joined illustrates the reliability relationship (e.g. series and parallel (redundant) configurations). More complex constructs are also available in order to create more realistic models (e.g. load sharing containers or standby containers).

RENO employs a visual and intuitive flowcharting approach to allow you to build models to analyze complex probabilistic or deterministic scenarios. With RENO, you can create scenarios graphically (through a flowchart type of interface), using a set of predefined constructs and function variables. Once the model has been constructed, simulation (through sequential execution of the flowchart) can be utilized. RENO is unique in the fact that it gives you the flexibility of a computer language, but instead of writing computer code, you use the familiar flowcharting concept to build your analysis (to write your "program"). This flexibility does require that the user is adept at building simulation scenarios from the ground up.

Which software tool is best for my application?
BlockSim provides a sophisticated discrete event simulation engine for reliability, maintainability, availability, throughput, life cycle cost summaries and related analyses. Additionally, an extensive array of RBD configurations and fault tree analysis gates and events, designed specifically for these types of analyses, are supported. Included are advanced capabilities to model complex configurations, load sharing, standby redundancy, phases, duty cycles, etc.

On the other hand, if you are trying to build a generic probabilistic simulation for applications including probabilistic risk analysis, event modeling, reliability analysis, financial analysis, forecasting, maintenance planning, optimization or operational research problems, then RENO provides that flexibility.

Where can I find more information about the software? Who should I contact if I have more questions about the differences and uses of BlockSim and RENO?
Our Web site has more detailed information about each of these software packages. Use the links below to find out more, or contact us directly.

* Does DOE++ offer variability analysis?

For two level full factorial, two level fractional factorial and Plackett-Burman designs with more than one replicate, DOE++ gives you the ability to determine the variability of the response(s) across runs and to analyze that standard deviation information. This offers valuable insight into the sources of variation within the experimental data, helping you to identify the treatment (or combination of factor settings) that results in the least amount of variation in the response(s) being studied.

To select the response(s) that you want to see variability analysis for, choose Data > Variability Analysis or click the Variability Analysis icon on the Main page of the Standard Folio Design tab Control Panel.

In the Variability Analysis window that appears, select the check box beside each response that you want to see variability analysis for. In the Factors to Include area, select the check box beside each factor that should be considered in the variability analysis. You can also select to consider blocks in the variability analysis.

For each selected response, a standard deviation column will be inserted beside it in the Standard Folio Design tab Data Sheet. This column displays the standard deviation of the observations taken for each factor setting combination across replicates. Such columns can be selected to be used as responses in the analysis.

For example, consider a two level full factorial design with three factors, A, B and C, and two responses. The following selections in the Variability Analysis window:

will yield a Standard Folio Design tab like the one shown next.

You can see that the standard deviation column that has been inserted for Response 1 includes a value for the standard deviation at the following settings:

Factor A Factor B
-1 -1
1 -1
-1 1
1 1

Factor C settings are ignored, as this factor was not selected for inclusion in the variability analysis.

Selecting the standard deviation column for Response 1 to be included in the analysis allows you to see how each factor setting combination affects the variability in Response 1, as shown next.

In the Regression Information table, AB is shown in red, indicating that the interaction of factors A and B is a significant source of variability in Response 1.

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