Reducing High generalization-error on industrial fault data

I have a industrial dataset containing labeled machine data for fault classification(3 classes: 1 ok, 2 for faults). The problem is that i have less (~16) different machines, thus iam currently having instance shift problems: The accuracies on the training set is perfect but validation on holdout instances fails. As background information, the machine data is time series, where i extracted statistical (domain specific) features from (14 in total). This features are my dataset for classification. I tried different model types, like SVM and MLP.

My question to you is: I tried to reduce the generalisation error with methods like Dropout and L1L2 regularisation - But this does not work well as it causes the training accuracy to stay low. It would be very helpful if you could get me some hints and tips would i could try. Thanks in advice.