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

Reference number
Coordinator Örebro universitet - Maskinteknik, Institutionen för naturvetenskap och teknik
Funding from Vinnova SEK 500 000
Project duration October 2017 - March 2018
Status Completed
Venture Strategic innovation programme for process industrial IT and automation – PiiA

Purpose and goal

The research question has been answered positively, since the project has shown new ways to model and simulate (parts of) the LKAB production process and tested a possible database structures to update the simulations with live data from machines in use. Therefore, the goals of the project are fulfilled.

Expected results and effects

The project results match the intentions at the time of the project application submission. New results regarding production process availability modelling, interfaced using a data base with reliability and maintainability sensor data, have been developed. ATCO has gained further insight for developing improved products forthwith, to be used in a predicative maintenance and productivity context. Alkit and Mobilaris have had input to further develop their products, and ORU has produced indata for research publications. All partners have developed their relationships further.

Planned approach and implementation

The PCM project was, according to plan, carried out jointly to specify the required system, facilitating knowledge exchange among the project actors through the joint approach. The PCM project has formed basis for continued development in future projects by all partners. Further research projects are planned. The three work packages (Project management, Availability & maintenance simulation process modelling, Information modelling & interfaces) have been sufficient to handle the project while at the same time avoiding excessive management.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 25 November 2019

Reference number 2017-03109

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