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Production Centred Maintenance for predictive maintenance decision support to maximise production efficiency

About this project

Project information

Project status



Magnus Löfstrand

Research subject

The project is carried out by the project coordinator Örebro University, Mechanical Engineering (ORU) in close collaboration with business partners Atlas Copco AB (ATCO), LKAB (LKAB), Alkit Communications AB (Alkit) and Mobilaris AB (MAAB) The PCM project application aims to demonstrate interfacing the competences from ORU, Alkit, and MAAB, in an ATCO application used in the LKAB production process. The industrial use case production process, an ATCO developed fleet of drill rigs, is part of one of LKAB’s two identical iron ore pellets production lines in Malmberget, Sweden. 

High reliability and availability are important industrial demands that are currently increasing in industrial significance. By monitoring of critical systems and system parameters, failures can be detected, predicted, and maintenance scheduled before damage occurs – known as predictive maintenance. However, current approaches therefore fail to combine a global view of production process with real time information on its state in sufficient detail that is necessary to provide decision-makers with the critical information they need to optimally schedule maintenance to meet business critical criteria rather than limited maintenance criteria scope. 

The objective of the project is to investigate how to interface state of the art technologies developed by the SME Alkit, for monitoring, modelling, analysis and secure communication of data obtained from sensors and telematics systems, with availability and maintenance simulation modelling tools developed by ORU and research partners  for prediction of production process availability and performance, to form an innovative, flexible and versatile software platform for improved predictive maintenance decision support. 

The question to be answered is:

Can state-of-the-art technologies in asset condition monitoring, asset tracking and production process availability simulation modelling be developed and interfaced to achieve improved maintenance and production scheduling capabilities? 

Thereby demonstrating more efficient management, reconfiguration and re-use of assets and resources while avoiding false alarms. If so, greater than 10% improvements in process downtime and maintenance costs are likely at LKAB. Answering the question above will be achieved through investigating the interfaces and data types necessary to define requirements for future development activities.

The business opportunity and goal is the future creation of a generic integrated suite of tools, this will be further developed in future research and development projects, which is fully in line with the research strategy of Mechanical Engineering at ORU. This will result in reduced disruption and downtime within the production process from equipment failures and poor maintenance scheduling. The benefits, if scaled to industrial implementation, include significant increases in production, reductions in maintenance costs and reduced risk of accidents. A scaled up future platform would be applicable to a wide range of production processes and engineering applications. Both process industries (i.e. mining, pulp & paper, sugar etc.) and engineering industries (heavy industrial machinery; rock drills, rock trucks, hydraulic motors, pumps etc.) are key areas of intended use.


Research funding bodies

  • PiiA
  • Vinnova