  This thesis explored applications of the new emerging techniques of
artificial intelligence and deep learning (neural networks in particular) for
predictive maintenance, diagnostics and prognostics. Many neural architectures
such as fully-connected, convolutional and recurrent neural networks were
developed and tested on public datasets such as NASA C-MAPSS, Case Western
Reserve University Bearings and FEMTO Bearings datasets to diagnose equipment
health state and/or predict the remaining useful life (RUL) before breakdown.
Many data processing and feature extraction procedures were used in combination
with deep learning techniques such as dimensionality reduction (Principal
Component Analysis) and signal processing (Fourier and Wavelet analyses) in
order to create more meaningful and robust features to use as an input for
neural networks architectures. This thesis also explored the potential use of
these techniques in predictive maintenance within oil rigs for monitoring
oilfield critical equipment in order to reduce unpredicted downtime and
maintenance costs.
