How can I view the properties of all blocks simultaneously in BlockSim?
The Item Properties Table in BlockSim allows the user to view and/or edit the properties of the blocks in the Diagram Sheet(s), Fault Tree Sheet(s) and Template(s) within the current project, along with any defined policies, in separate worksheets. This provides you with another way of viewing and editing properties without having to open the properties windows for each item defined in a project (e.g. Block Properties window, Crew Policy window, etc.). Any changes made to properties in the Item Properties Table will be applied to the corresponding properties window and vice versa.
The Item Properties Table can be accessed by selecting Item Properties Table from the Project menu in the MDI or the Project Explorer shortcut menu. You can choose the specific properties to be displayed by clicking the double check mark button, , and checking the desired properties to be viewed. The Item Properties Table is shown next.
In Weibull++, when fitting a distribution with a location parameter, should the location parameter always be equal to the minimum failure time?
Some distributions, such as the three-parameter Weibull and the two-parameter exponential distributions, can have a location parameter, usually called γ (gamma), which signifies a shift of the beginning of the distribution by a distance of γ. If γ > 0 the distribution is shifted to the right, indicating that chance failures start to occur only after γ hours of operation, and cannot occur before.
Lets consider the following data set and try to fit a 3-parameter Weibull distribution to it:
Using MLE as the parameter estimation method, the following parameters are obtained:
One might wonder why γ = 82.25 hours although the data set suggests that no failures happened before 100 hours. Weibull++ tries to come up with the best fit for the failure data using the selected distribution and parameter estimation method. It is not the lowest failure time that dictates the value of the γ parameter; rather, it is the collection of the whole data set which has randomness. Therefore, it is possible that the best fits γ parameter would deviate from the lowest failure time in the data set.
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