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No, it is also useful for topics like production quality control, maintenance optimisation, failure prediction, stock adjustment, and more.
No, as a matter of fact, it’s easier and less expensive to test specific subsystems or components that have special criticality or are expected to fail sooner.
In this case, the more the better, as with fewer units the results are less precise and the confidence bounds grow. However, there is not an exact number of required units and it all depends on what kind of results can be useful for your case. Some tests with 5 to 10 failures have proved to be useful in plenty of cases.
No, you can continuously add information to your Broadstat projects, so you can start a project with the information you have at one point, and keep adding new failure data whenever you have it. Your project can be a live study that gets more precise over time.
As long as you are sure they come from the same failure mode and that the operating conditions are equivalent, of course you can. In fact, if you detect early failures coming from client reports, you could try to replicate them on your lab and use both sets of data to create a project and obtain actionable results.
The part of creating the project, introducing the data, and performing the calculations is very straight forward, and Broadstat can help you do it in a matter of minutes. However, the longest part tends to be the obtention of your failure data. If you already have it from a collection of incidence calls from your clients, you are good to go, but if you set up an experiment from scratch, you’ll need to wait until those units fail.
Anything, from mechanical parts, to actuators, electronics, etc. that suffers deterioration or lack of function caused by use over time. It can be produced by repeated cycles of stress or continuous operation, and the failure modes can be very diverse.