  Time-series data are one of the fundamental types of raw data representation
used in data-driven techniques. In machine condition monitoring, time-series
vibration data are overly used in data mining for deep neural networks.
Typically, vibration data is converted into images for classification using
Deep Neural Networks (DNNs), and scalograms are the most effective form of
image representation. However, the DNN classifiers require huge labeled
training samples to reach their optimum performance. So, many forms of data
augmentation techniques are applied to the classifiers to compensate for the
lack of training samples. However, the scalograms are graphical representations
where the existing augmentation techniques suffer because they either change
the graphical meaning or have too much noise in the samples that change the
physical meaning. In this study, a data augmentation technique named ensemble
augmentation is proposed to overcome this limitation. This augmentation method
uses the power of white noise added in ensembles to the original samples to
generate real-like samples. After averaging the signal with ensembles, a new
signal is obtained that contains the characteristics of the original signal.
The parameters for the ensemble augmentation are validated using a simulated
signal. The proposed method is evaluated using 10 class bearing vibration data
using three state-of-the-art Transfer Learning (TL) models, namely,
Inception-V3, MobileNet-V2, and ResNet50. Augmented samples are generated in
two increments: the first increment generates the same number of fake samples
as the training samples, and in the second increment, the number of samples is
increased gradually. The outputs from the proposed method are compared with no
augmentation, augmentations using deep convolution generative adversarial
network (DCGAN), and several geometric transformation-based augmentations...
