The Hyperconnected Architecture for High Cognitive Production Plants (HyperCOG) project aims at the process industry’s complete digital transformation through an advanced Industrial Cyber-Physical Infrastructure. It is based on advanced technologies that allow a hyperconnected network of digital nodes to be created improving the classic automation hierarchy of communication layers. The nodes will collect data streams in real-time, offering cognitive sensing and information along with high performance computing capabilities making the process industry businesses solid in different scenarios. The system is validated in three fields of the process industry: steel, cement and chemical where optimisation in the use of energy and raw materials is obtained, among other benefits.
The HyperCOG project addresses the full digital transformation of process industry through an innovative Industrial Cyber-Physical System and Data Analytics. It is based on advanced technologies that enable the development of a hyperconnected network of digital nodes. The nodes can catch outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. The breaking-edge system proposed in this work is validated on productivity, environmental and replicability aspects on three use cases of three different sectors: steel, cement and chemical.
For two centuries, the industrial sector has never stopped evolving. Since the dawn of the Fourth Industrial Revolution, commonly known as Industry 4.0, deep and accurate understandings of systems have become essential for real-time monitoring, prediction, and maintenance. In this paper, we propose a machine learning and data-driven methodology, based on data mining and clustering, for automatic identification and characterization of the different ways unknown systems can behave. It relies on the statistical property that a regular demeanor should be represented by many data with very close features; therefore, the most compact groups should be the regular behaviors. Based on the clusters, on the quantification of their intrinsic properties (size, span, density, neighborhood) and on the dynamic comparisons among each other, this methodology gave us some insight into the system’s demeanor, which can be valuable for the next steps of modeling and prediction stages. Applied to real Industry 4.0 data, this approach allowed us to extract some typical, real behaviors of the plant, while assuming no previous knowledge about the data. This methodology seems very promising, even though it is still in its infancy and that additional works will further develop it.
We go deeper and deeper in automation: for decades, and even centuries, we have been dreaming of a completely automated world with the capacity to self-adapt to any imaginable context. A very promising paradigm to do so is Machine Learning, which allows an automatic and often reliable modeling of any unknown system, by analyzing its data and by deriving knowledge from them. The first tasks of such an approach are data preparation and knowledge extraction, which are areas of research on themselves; as such, experts are generally required in order to manually identify and characterize the studied systems, what can be a long, exhaustive and expensive task. Wouldn't be great if this step could be executed automatically? In this paper, we will present some avenues of reflection in order to achieve such a cognitive system, by digging into the data through clustering approaches and by extracting any knowledge possible.
The new challenges Science is facing nowadays are legion; they mostly focus on high level technology, and more specifically Robotics, Internet of Things, Smart Automation (cities, houses, plants, buildings, etc.), and more recently Cyber-Physical Systems and Industry 4.0. For a long time, cognitive systems have been seen as a mere dream only worth of Science Fiction. Even though there is much to be done, the researches and progress made in Artificial Intelligence have let cognition-based systems make a great leap forward, which is now an actual great area of interest for many scientists and industrialists. Nonetheless, there are two main obstacles to system’s smartness: computational limitations and the infinite number of states to define; Machine Learning-based algorithms are perfectly suitable to Cognition and Automation, for they allow an automatic – and accurate – identification of the systems, usable as knowledge for later regulation. In this paper, we discuss the benefits of Ma chine Learning, and we present some new avenues of reflection for automatic behavior correctness identification through space partitioning, and density conceptualization and computation.