Ensemble Learning for Predictive Maintenance
|Koordinator||Högskolan i Halmstad - CAISR - Center for Applied Intelligent Systems Research|
|Bidrag från Vinnova||115 834 kronor|
|Projektets löptid||november 2018 - februari 2019|
|Utlysning||Individrörlighet för innovation och samhällsnytta|
|Ansökningsomgång||Arbeta tillfälligt i annan bransch - innovation inom artificiell intelligens (AI)|
Syfte och mål
The work has resulted in improved models for predictive maintenance, implemented through integration of several different techniques and using multiple data sources. The ensemble model created in this project, which combines classifiers from different data domains, leads to improved accuracy and recall for several different components. Combining the complementary features allows us to decompose a complex, difficult learning problem into easier sub-problems.
Resultat och förväntade effekter
The existing collaboration between Halmstad University and Volvo Group, in particular Volvo Trucks Aftermarket, has been strengthened significantly through this mobility grant. The knowledge exchange in both directions has lead to improved technical solutions which now allow for more precise estimation of the failure risk of various components.
Upplägg och genomförande
The project consisted of efforts attempting to answer two research questions. First, what are the constraints and challenges of integrating ensemble approach into existing model? Second, how diverse are individual models based on each data domain, and how could we improve this diversity among models? Both questions have proven valuable and provided important insights into the final developed solution.