Identify / separate sorts of failure I ran a Weibull analysis on data of devices which consists mainly of failures over time (80 % died, 20 % are still alive). In total there are around ~4500 devices.
After conducting this analysis I received this plot by means of WeibullR:

After some digging on the internet I read here and there that this shape of the curve means, that there are different failure reasons within the data.
So, my objective now is to separate these different failure mechanisms. Surely it's not given in the data. Is there a way backward, like using the plot resp. the distribution somehow to separate them reasonably?

If your domain knowledge suggests that there are different failure modes possible but you can't identify the mode directly in a failed device, you might consider a parametric mixture model that allows for different baseline hazards. In principle that could help distinguish survival functions associated with different failure modes, although it couldn't tell you precisely which device failed via which mode.
I don't have experience with such models. A quick search shows the mixPHM package for mixtures of parametric proportional-hazard models, which includes your Weibull model. That package requires you to specify how many components to include in your mixture. There are presumably ways to estimate the number of components from the data too, but that's far outside my expertise.
