PhD. Vacancy “Industrial Quality Monitoring based on Statistical Process Control (SPC) Technique

PhD. Vacancy “Industrial Quality Monitoring based on
Statistical Process Control (SPC) Techniques”
Description:
Statistical Process Control (SPC)
is a widely used framework for monitoring industrial processes. Within SPC, control charts are a powerful
tool to detect deviations from normal operation, although they are mainly fit
to processes with a fixed target. Such
fixed target, however, is not present in numerous applications and the proposed
research will focus on such nonstationary processes. Examples are ubiquitous and include production
processes in the agro-food sector but also within the framework of monitoring
industrial equipment that is subject to degradation.
Part of the research fits in the
framework of an interdisciplinary research project entitled “Prognostics for
Optimal Maintenance (POM)”. It is
coordinated by the Flanders Mechatronics Technology Centre (FMTC). The goal of the project is toprovide the tools needed to improve
the way maintenance of industrial machinery is done. Today maintenance is regarded as a necessary
burden to keep the machines running. However,
companies agree that it would be much better if they could step down from this
image and innovate the way machines and
maintenance are sold. The POM consortium will leverage on the experience the
industry has gained in order to solve a complex optimization problem:
how to provide a better maintenance of industrial machines at a lower total
cost of ownership. POM will develop tools and methodologies that will
help machine builders to reduce the costs and market the whole assemble of
machine plus maintenance. Our team is
mainly involved in developing Statistical Process Monitoring algorithms to
detect anomalies during machine operation. Those anomalies then serve as input for lifetime prediction models
developed by our partners.
Keywords: Statistical
Process Control (SPC), nonstationary processes, degradation
Profile: The candidate should preferably hold a degree
in Statistics, Bioscience Engineering, Engineering or Physics.
Interested? Please send your CV and motivation letter to bart.deketelaere@biw.kuleuven.be. We will contact you soon!