In this paper, a methodology based on machine
learning for fault detection in continuous processes is presented.
It aims to monitor fully distributed scenarios, such as the
Tennessee Eastman Process, selected as the use case of this work,
where sensors are distributed throughout an industrial plant. A
hybrid feature selection approach based on filters and wrappers,
called Hybrid Fisher Wrapper method, is proposed to select the
most representative sensors to get the highest detection quality
for fault identification. The proposed methodology provides a
complete design space of solutions differing in the sensing effort,
the processing complexity, and the obtained detection quality.
It constitutes an alternative to the typical scheme in Industry
4.0, where multiple distributed sensor systems collect and send
data to a centralised cloud. Differently, the proposed technique
follows a distributed approach, in which processing can be done
eventually close to the sensors where data is generated, i.e., at
the edge of the Internet of Things. This approach overcomes
the bandwidth, privacy, and latency limitations that centralised
approaches may suffer. The experimental results show that
the proposed methodology provides Tennessee Eastman Process
fault detection solutions with state-of-the-art detection quality
figures. In terms of latency, solutions obtained outperform in
37.5 times the implementation with the highest detection quality,
using 1.99 times fewer features, on average. Also, the scalability
of the framework provides a design space where the optimal
implementation can be chosen according to the application needs
