  Recent trends focusing on Industry 4.0 concept and smart manufacturing arise
a data-driven fault diagnosis as key topic in condition-based maintenance.
Fault diagnosis is considered as an essential task in rotary machinery since
possibility of an early detection and diagnosis of the faulty condition can
save both time and money. Traditional data-driven techniques of fault diagnosis
require signal processing for feature extraction, as they are unable to work
with raw signal data, consequently leading to need for expert knowledge and
human work. The emergence of deep learning architectures in condition-based
maintenance promises to ensure high performance fault diagnosis while lowering
necessity for expert knowledge and human work. This paper presents developed
technique for deep learning-based data-driven fault diagnosis of rotary
machinery. The proposed technique input raw three axis accelerometer signal as
high-definition image into deep learning layers which automatically extract
signal features, enabling high classification accuracy.
