  Timely failure detection for bearings is of great importance to prevent
economic loses in the industry. In this article we propose a method based on
Convolutional Neural Networks (CNN) to estimate the level of wear in bearings.
First of all, an automatic labeling of the raw vibration data is performed to
obtain different levels of bearing wear, by means of the Root Mean Square
features along with the Shannon's entropy to extract features from the raw
data, which is then grouped in seven different classes using the K-means
algorithm to obtain the labels. Then, the raw vibration data is converted into
small square images, each sample of the data representing one pixel of the
image. Following this, we propose a CNN model based on the AlexNet architecture
to classify the wear level and diagnose the rotatory system. To train the
network and validate our proposal, we use a dataset from the center of
Intelligent Maintenance Systems (IMS), and extensively compare it with other
methods reported in the literature. The effectiveness of the proposed strategy
proved to be excellent, outperforming other approaches in the state-of-the-art.
