Edge Analytics for Smart Diagnostics in Digital Machinery Concept

Project Details


Today, increasing part of added value for new technical solutions comes from digitalization and advanced automation. Using Big data and cloud analytics, machines can be made more reliable, more energy efficient and the operation can be optimized. In this project, the target is to transfer the capabilities of Big data and cloud processing to the Edge, enabling real-time safety-critical operation, regardless of communication availability and at the same time minimising data transfer costs. This provide technology for reliable, energy efficient and environmentally-friendly solutions.

The developed solutions and methods are utilised for the needs of machine industry and analytics solutions enables smart diagnostics, predictive maintenance and optimisation of operation. In this project, edge analytics that utilise information from the designing phase of an equipment and physical context (Digital twin), feedback of expert personnel (human-in-the-loop) and capacity of cloud environment in training, initialisation and parametrisation, are reached for, so that optimal up-to-date analytics can be guaranteed (see Figure 1). The developed solutions and methods will be verified and validated using demonstrators (ship engine, forest machine, shipyard crane) and data collected from these systems.
Short titleEDGE
Effective start/end date01/10/1828/02/21

Collaborative partners

  • Åbo Akademi University
  • Tampere University (Project partner) (lead)
  • University of Vaasa (Project partner)
  • Wärtsilä (Finland) (Project partner)
  • Wapice (Finland) (Project partner)
  • Fingrid (Project partner)
  • Quant sataservice (Project partner)
  • Solita (Project partner)