Due to the advancements in the Information and Communication Technologies field in the
modern interconnected world, the manufacturing industry is becoming a more and more
data rich environment, with large volumes of data being generated on a daily basis, thus
presenting a new set of opportunities to be explored towards improving the efficiency and
quality of production processes.
This can be done through the development of the so called Predictive Manufacturing
Systems. These systems aim to improve manufacturing processes through a combination
of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time
Data Analytics in order to predict future states and events in production. This can be used
in a wide array of applications, including predictive maintenance policies, improving quality
control through the early detection of faults and defects or optimize energy consumption,
to name a few.
Therefore, the research efforts presented in this document focus on the design and development
of a generic framework to guide the implementation of predictive manufacturing
systems through a set of common requirements and components. This approach aims
to enable manufacturers to extract, analyse, interpret and transform their data into actionable
knowledge that can be leveraged into a business advantage. To this end a list
of goals, functional and non-functional requirements is defined for these systems based
on a thorough literature review and empirical knowledge. Subsequently the Intelligent
Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with
a detailed description of each of its main components.
Finally, a pilot implementation is presented for each of this components, followed by the
demonstration of the proposed framework in three different scenarios including several use
cases in varied real-world industrial areas. In this way the proposed work aims to provide
a common foundation for the full realization of Predictive Manufacturing Systems
