Abstract:
Maintenance strategies based on condition monitoring of the different machines
and devices in an industrial process can minimize downtime, increase the
safety of plant operations and help in the process of decision-taking for control
and maintenance actions in order to reduce maintenance and operating costs.
Multivariate statistical methods are widely used for process condition monitoring
in modern industrial sites due to the quantity of data available and the difficulties
of building analytical models in complex facilities.
Nevertheless, the performance of these methodologies is still far away from
being ideal, due to different issues such as process nonlinearities or varying
operational conditions. In addition application of the latest approaches
developed for process monitoring is not widely extended in real industry.
The aim of this investigation is to develop new and improve existing
methodologies for predictive condition monitoring through the use of
multivariate statistical methods. The research focuses on demonstrating the
applicability of multivariate algorithms in real complex cases, the improvement
of these methods in terms of fault detection and diagnosis by means of data
fusion and the estimation of process performance degradation caused by faults.
