Predictive Maintenance for Service Business Models and Extended Product Life
|Coordinator||Örebro universitet - Maskinteknik, Institutionen för naturvetenskap och teknik|
|Funding from Vinnova||SEK 489 900|
|Project duration||November 2017 - May 2018|
|Venture||The strategic innovation programme for Production2030|
Purpose and goal
A problem is that available predictive maintenance technologies focus on condition monitoring and optimising maintenance schedules of individual assets in isolation, thus existing systems don’t provide decision-makers with production critical information (downtime) needed to optimally schedule maintenance to meet service business critical criteria. Hence the project evaluated requirements for the development of existing models to support service-based business models in engineering industry, and developed technical requirements, supporting the mining industry.
Expected results and effects
Main prerequisites for adoption of service business models (where product lifetime, maintenance efficiency and production availability must be predicted and optimised) have been identified. A needs requirements description regarding maintenance decision support in terms of predictive/preventive maintenance has been carried out. Further, interface requirements descriptions for the software developed as required by ATCO drill rigs in a service business model context. The project deliverables have therefore been met.
Planned approach and implementation
The PMBM 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 project has formed the basis for continued development in future projects by all partners. Further research projects are planned. The four work packages (Project management, Service business models and extended product life, Availability & maintenance simulation process modelling, Information modelling & interfaces) have been sufficient to handle the project while avoiding excessive management.