There's no point to having an ML model unless you are applying it to something outside of the training data.
If you plan on applying the model to different turbines, then there is potential for sample bias in which turbines you selected. If you apply it to the same turbines at some point in the future, then you sampled points in time so there is a potential for sample bias based on which points in time you selected.
There is no way of completely avoiding the potential for sample bias unless you completely abandon ML as a useful concept.
Why would I care about the fact that only 10% of turbines globally have Siemens sensors? I don't know the failure data outside of the turbines I own and operate, and those are the only ones I need to predict failures for.
Say that turbines have an average lifespan of X years, and from year 0 to 10 you bought 90% Siemens and then from year 10 to 20 you bought 10% Siemens and then you measure failure rates from year X to year X+10.
Based on that data you would predict that Siemens turbines will be the most likely to fail next, but they are probably actually less likely to fail because most of the ones that are likely to fail soon are already gone.
