Thanks for your reply 
Alright, yeah I was afraid that that could be the case. What would be an appropriate amout of training data to get a halfway decent result when testing ?
I got 560 samples of accelerometer readings each containing 133328 datapoints. From that I extracted 16 features for each sample. Unfortunately the data is highly imbalanced where 6/7 is data where the machine is in bad condition and 1/7 where the machine is in good condition.
Up to now I went with undersampling and threw away 5/7 of the “bad” data.
Right now I try to split each samples in 7 subsamples and get the features of that. I would then throw away the 6/7 of the bad data again but would end up with equal amounts of good and bad data (560 samples of good, 560 samples of bad), which are 1/7 of the original samples length. For training I would then have 504 dataset and 56 for testing. Is that sufficient, or do you have other suggestions how to “generate” more input data ? Hope you can follow me, I am not a native speaker. Thanks in advance