  Given the growing amount of industrial data spaces worldwide, deep learning
solutions have become popular for predictive maintenance, which monitor assets
to optimise maintenance tasks. Choosing the most suitable architecture for each
use-case is complex given the number of examples found in literature. This work
aims at facilitating this task by reviewing state-of-the-art deep learning
architectures, and how they integrate with predictive maintenance stages to
meet industrial companies' requirements (i.e. anomaly detection, root cause
analysis, remaining useful life estimation). They are categorised and compared
in industrial applications, explaining how to fill their gaps. Finally, open
challenges and future research paths are presented.
