allmän projektillustration

Project: Predictive Cognitive Maintenance Decision Support system (PreCoM)

This project aims to develop, test, implement and demonstrate in three manufacturing companies a new predictive maintenance system with intelligence and self-learning. Its goal is to reduce failures and unnecessary stoppages, optimize maintainability, and increase the machines’ availability and reliability in a cost effective manner.

Project coordinator
Basim Al-Najjar
Other project members at Linnaeus University
Francesco Barbabella, Hatem Algabroun
Participating organizations
17 members from six European countries: Linnaeus University (Sweden), E-maintenance Sweden AB (Sweden), Paragon (Greece), Savvy Data Systems (Spain), Vertech Group (France), Bosch (Germany), Soraluce S.Coop (Spain), Sakana S.Coop (Spain), Overbeck GmbH (Germany), Spinea s.r.o. (Slovakia), Gomà-Camps S.A.U. (Spain), Lantier S.L. (Spain), Ideko S.Coop (Spain), Commissariat à l'énergie atomique et aux énergies alternatives (France), Consorcio Instituto Tecnológico de Matemática Industrial (Spain), Technische Universität München (Germany), Technische Universität Chemnitz (Germany)
EU Horizon 2020, FoF 09
1 Nov 2017-31 Oct 2020
Terotechnology (Department of Mechanical Engineering, Faculty of Technology)

Basim Al-Najjar, professor of terotechnology and project leader, presents the PreCoM project
An animated video explaining more about what the PreCoM project is about


More about the project

Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15 per cent of their total maintenance costs on predictive (vs reactive or preventative) maintenance.

The project will deploy and test a predictive, cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct necessary maintenance actions and ultimately increase in-service efficiency of machines by at least 10 per cent.

The platform includes four modules:

  1. A module for data acquisition leveraging external sensors as well as sensors directly embedded in the machine tool components.
  2. An artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health conditions and supporting a large range of assets and dynamic operating conditions.
  3. A secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities.
  4. A human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks.

The consortium includes three end-user factories, three machine-tool suppliers, one leading component supplier, four innovative SMEs, three research organizations and three academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.