How can I use BlockSim 7 to
figure out how to achieve a system reliability goal?
When developing a new product, an engineer often is given the
reliability target and must then develop a design that will achieve the desired system
reliability while performing all of the system's intended functions at a minimum cost.
This involves a balancing act of determining how to allocate reliability to the components
so the system will meet its reliability goal, while at the same time ensuring that the
system meets all of the other associated performance specifications.
BlockSim 7’s optimization
calculations can help to determine the optimum scenario to increase the reliability of one
or more individual components in order to achieve the system reliability goal that you
To perform optimization calculations for the current RBD or fault tree, you must first
decide which blocks will be included in the calculations. For each block that you want to
include, select the Block to be used in optimized allocation check box on the
Reliability Allocation page of the Block Properties window. In addition, enter the
Maximum Achievable Reliability for the block and specify the feasibility of
improvement (i.e., how difficult or costly it is to increase the reliability of the
block). Feasibility may be specified by dragging the slider on the scale of Easy to Hard
(relative to the difficulty/cost of improving other blocks in the system), or by a
user-defined cost function.
Once you have specified the Reliability Allocation properties for each block that you
want to include in the calculations, open the Analytical QCP. On the Optimization page,
enter the following:
The Mission End Time is the time associated with the target reliability.
For example, if you want to determine the optimum scenario for increasing component
reliability in order to achieve system reliability of .98 at 100 days, you would
enter 100 as the mission end time.
The Reliability Goal is the system reliability that you are trying to
reach at the specified time. This number must be greater than 0 and less than 1. For
example, if you want to determine the optimum scenario for increasing component
reliability in order to achieve a system reliability of .98 at 100 days, you would
enter .98 as the reliability goal.
The Iterations field allows you to specify the maximum number of
iterations of the optimization algorithm that may be conducted in order to obtain a
The results of the optimization calculations are displayed in the Results Panel, as
For each block considered in the optimization calculations, the following results are
Reliability(Mission End Time) is the current reliability for the
component as calculated at the specified mission end time.
Goal(Mission End Time) is the reliability of the component that would be
required in the optimum scenario to reach the specified system reliability
N.E.P.U. (i.e., Number of Equivalent Parallel Units) is the number of
identical blocks that you would have to place in a parallel configuration in order for
the particular block to meet the specified reliability goal, in lieu of increasing
the component’s inherent reliability.
For the system overall, the System Reliability row displays the calculated reliability
and the reliability that could be expected if the optimum scenario were implemented.
For more information on optimization calculations, please refer
In Weibull++ 7, is there a way to
perform a quick what-if analysis on a failure model without supplying data?
You can perform what-if analyses
in Weibull++ 7 even when the only
information you have is the distribution and its parameters. There are two methods
available: analyze the distribution without using any data at all or generate a
simulated data set that is distributed according to the model.
To analyze a distribution without using a data set, create a Weibull++ standard
folio data sheet but do not enter any data. On the control panel, choose a distribution
for the analysis and click the Calculate icon. The application will prompt you to
enter the parameters. The following example shows the input window for a 2-parameter
Specify values for the parameters and click OK. You can use the plots and the
QCP in the same way that you would for a calculated data set; however, calculations
involving confidence bounds are not available.
To generate a simulated data set that is distributed according to a model,
choose Tools > Generate Monte Carlo Data. Select a distribution, enter its
parameters and enter the number of points to generate, as shown next.
Click Generate to create the simulated data set. You can then analyze the
simulated data set then use the plots and QCP to obtain results. Confidence bounds
calculations are available when you use this method.