Smart Industry Group

Smart Industry Group (SIG) is an interdisciplinary research group featuring expertise from computer science and mechanical engineering. SIG's focus is making production and products in industry smart using cyber-physical systems, that is, systems that intertwine software and physical components.

The advent of sensory technologies (e.g., Internet of Things; IoT) combined with the possibilities of the new communication networks like 5G is creating the necessary infrastructure for the Cyber-Physical Systems (CPS). In CPS, software and physical components are deeply intertwined. Such a convergence of the cyber and physical worlds offers boundless opportunities to all areas of industry – manufacturing, logistic, building, energy, health, etc – by becoming the cornerstone of future Industry 4.0.

The research group Smart Industry Group (SIG) is an interdisciplinary partnership between the Department of Computer Science and Media Technology and the Department of Mechanical Engineering, bringing together expertise in the cyber and physical world, respectively.

SIG is interested in leveraging data-driven techniques for making production and products smart, i.e., context-aware, self-aware, and adaptive. For example, a production system can automatically adjust to the desired production level with optimal logistics and flow at the lowest cost, whereas a product can automatically reconfigure with respect to operation and/or human factors.

The integration of CPS elements in production systems and products allows for offering new appealing functionality to the different stakeholders, i.e., operators and end-users, respectively. At the same time, the integration of CPS elements also increases the complexity of production systems and products, and seriously complicates the assessment and assurance of key quality aspects. Such aspects may be safety, security, performance, sustainability (i.e., near-zero power consumption), maintainability, and usability.

To this end, the Smart Industry Group aims at investigating:

  1. Data-driven discovery techniques for inferring production/products models from data.
  2. Data-driven analysis techniques for inferring production/products quality aspects from models and data.

Projects

Seed projects

At Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), we encourage and support seed projects. Seed projects are intended to promote and nurture excellence research, development and innovation within data intensive sciences and applications with cross-discipline collaboration.

Seed projects at CPS

Towards a data-driven approach to ground-fault location

Staff