PhD Thesis

Enhancing maintenance and energy efficiency in smart manufacturing processes through non-intrusive monitoring strategies

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  • Started: 01/09/2019
  • Thesis project read: 10/11/2020


This project is oriented to the development and implementation of automatic control methodologies to improve the energy efficiency of industrial factories, mainly focusing on both machine and line levels and considering devices of different nature (capacitive, resistive and inductive). Regarding the compositional devices interacting in a machine (or line of machines), their nonlinear phenomena from the point of view of energy consumption as well as the limited possibilities of sensing key variables make the problem challenging and state difficult milestones for the design of the proposed advanced control strategies. In particular, the main challenges raised by the project are to propose identification procedures to determine the models of energy consumption of the plant (either machine or line), to formally analyse how these models will evolve to be suitable facing disturbances, and to implement in real time the resultant control strategies based on the identified models, which imply the embedding of the corresponding control approaches into devices such as FPGAs, microcontrollers, among others. Aingura IIoT has the required test benches for the validation of such control strategies over machines and the production lines, which will be fully available for the doctoral project.

The work is under the scope of the following projects:

  • IKERCON: Control avanzado de procesos complejos de manufactura (web)