Dissertação para obtenção do Grau de Mestre em
Engenharia Electrotécnica, Sistemas e ComputadoresThe dissertation presents the work done under the scope of the NP7 Self-Learning
project regarding the design and development of the Adapter component as a foundation
for the Self-Learning Production Systems (SLPS). This component is responsible to confer additional proprieties to production systems such as lifecycle learning, optimization of process parameters and, above all, adaptation to different production contexts. Therefore, the SLPS will be an evolvable system capable to self-adapt and learn in response to
dynamic contextual changes in manufacturing production process in which it operates.
The key assumption is that a deeper use of data mining and machine learning techniques
to process the huge amount of data generated during the production activities will allow
adaptation and enhancement of control and other manufacturing production activities
such as energy use optimization and maintenance. In this scenario, the SLPS Adapter acts as a doer and is responsible for dynamically adapting the manufacturing production system parameters according to changing manufacturing production contexts and, most important, according to the history of the manufacturing production process acquired during SLPS run time.To do this, a Learning Module has been also developed and embedded into the SLPS Adapter. The SLPS Learning Module represents the processing unit of the SLPS Adapter and is responsible to deliver Self-learning capabilities relying on data mining and operator’s feedback to up-date the execution of adaptation and context extraction at run time.
The designed and implemented SLPS Adapter architecture is assessed and validated
into several application scenario provided by three industrial partners to assure industrial relevant self-learning production systems. Experimental results derived by the application of the SLPS prototype into real industrial environment are also presented
