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.
With the “new data”, as mentioned in the last post, I end up with a rather consistent accuracy of about 85%. Also not great yet, but I think its going in the right direction.
The dataset I used is the PHM Society, Gearbox fault detection data set, 2010. This was used for an international challenge where with that dataset the general health and specific faults of the gearbox for each of the 560 runs should be classified.