Gerrit:

Just out of curiosity, it seems that also the “quality” of the dataset has an influence on the results. So for my example rather than just using the raw sensor readings as inputs for the NN, I went with extracting features of the different runs that might describe the condition of the gearbox more accurately and used this as an input vector.


Yes, feature engineering helps in a lot of use cases and is a common way to improve the performance of an ML model. Often it’s expected that “deep” models are able to extract useful features automatically, so less effort is spent on the manual feature engineering.
However, if you are working on “smaller” models you would often still benefit from it.
E.g. a very simple (and artificial) example would be the dataset in Figure on the top left from these scikit-learn docs 2. Assume you have these two “rings” as two separate classes. Passing the features as e.g. the angle of each point wouldn’t work in a simple classifier without transforming the input space. However, if you pass the magnitude (norm) of each point to the classifier, it would most likely be able to yield good results.