Speaker
Description
Since the last decade, the so-called Fourth Industrial Revolution is
ongoing. It is a profound transformation in industry, where new tech-
nologies such as smart automation, large-scale machine-to-machine com-
munication, and the internet of things are largely changing traditional
manufacturing and industrial practices. The analysis of the huge amount
of data, collected in all modern industrial plants, not only has greatly
benefited from modern tools of artificial intelligence, but has also spurred
the development of new ones. In this context, we present a new approach,
based on the combined use of a Long Short-Term Memory (LSTM) neu-
ral network and Bayesian inference, for the predictive maintenance of an
industrial plant. SPE and Hotelling metrics, assessing the degree of com-
patibility between the time-evolving industrial data and the output of the
LSTM, trained on a reference period of good working condition, are used
to update the Bayesian probability of a failure of the plant. This method
has successfully been applied to a real industrial case and the results are
presented and discussed. Finally, it is important to highlight that, although
developed to tackle a precise industrial need, the presented approach is
general and can be applied to a plethora of other scenarios.
