Flexible Models for Smart Maintenance
|Coordinator||Högskolan i Gävle - Akademin för Teknik och Miljö|
|Funding from Vinnova||SEK 493 200|
|Project duration||November 2017 - June 2018|
|Venture||The strategic innovation programme for Production2030|
Purpose and goal
Maintenance in existing plants is becoming increasingly important, where predictive maintenance has become an emerging technology. The use of decision support tools contributes to environmentally and economically sustainable production. Within this project, different types of digital twins have been designed and evaluated. Specifically, new predictive model types have been tested in two different industrial case studies. The model showing best overall results is the LAVA model. In addition, a technical platform has been identified for implementation in existing plants.
Expected results and effects
The aim has been to find general methods for smart maintenance in existing industrial plants. The methods have been evaluated for both usability in specific applications and how well they can be generalized for any industrial plant. The model showing best overall results is the LAVA model, which is a Black box model. The advantage of black box models is that they are general; however, process knowledge is still necessary for implementation. The results from the project are promising, but a longer test period is required to rule out e.g. seasonal variations.
Planned approach and implementation
The two case studies are a heat exchanger on SSAB and a profiled header on Svenska Fönster AB. In addition, a laboratory environment at the University of Gävle has been built since it has not been possible to try different methods to detect (and perhaps even initiate) errors in industrial plants that were in operation. Measured data from the plants have been acquired from existing control systems and with a complementary measurement system. The modeling work has been done offline and analysis of the results has been done jointly by model developers and staff with process knowledge.