  The increased presence of advanced sensors on the production floors has led
to the collection of datasets that can provide significant insights into
machine health. An important and reliable indicator of machine health,
vibration signal data can provide us a greater understanding of different
faults occurring in mechanical systems. In this work, we analyze vibration
signal data of mechanical systems with bearings by combining different signal
processing methods and coupling them with machine learning techniques to
classify different types of bearing faults. We also highlight the importance of
using different signal processing methods and analyze their effect on accuracy
for bearing fault detection. Apart from the traditional machine learning
algorithms we also propose a convolutional neural network FaultNet which can
effectively determine the type of bearing fault with a high degree of accuracy.
The distinguishing factor of this work is the idea of channels proposed to
extract more information from the signal, we have stacked the Mean and Median
channels to raw signal to extract more useful features to classify the signals
with greater accuracy.
