Vibration data is one of the most informative data to be used for fault detection. It mostly employs in the form of frequency response function (FRF) for training deep learners. However, since normally the FRFs are measured at excessive numbers of frequencies, its usage not only enforces large computational resources for training the deep learners, but could also hinder proper feature extraction. In this paper, it is shown that given a predefined deep learning structure and its associated hyper-parameters, how proper data selection and/or augmentation could improve the performance of the trained model in classifying the samples. For this purpose, the least absolute shrinkage and selection operator (LASSO) and some generative functions are utilized respectively for data selection/reduction and augmentation prior to any training. The efficacy of this procedure is illustrated by applying it to an experimental dataset created by the broadband vibrational responses of polycrystalline Nickel alloy first-stage turbine blades with different types and severities of damages. It is shown that the data selection and augmentation approach could improve the performance of the model to some extent and at the same time, drastically reduce the computational time.