Reference number : GC-2017-Q4-00603, amendment number : , agreement type : G, recipient legal name : Fowler, Michael (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : This Engage project aims to establish a new collaboration between Acerta Analytics Solutions and the research_x000D_
team supported by Dr. Michael Fowler. The two parties have not engaged before in any form of collaboration_x000D_
to date. Advances in computing technology enable new forms of data analysis. Acerta offers analytics_x000D_
solutions to identify warranty issues of manufactured goods at the end of life. This means that Acerta's_x000D_
algorithms analyze data collected from the manufactured good before it leaves the factory and based on the data_x000D_
determines whether the good should be shipped to the customer or whether it will likely have a warranty issue_x000D_
and thus should be inspected in the factory prior to shipping. Currently, Acerta is applied to products using_x000D_
gearboxes, engines, and bearings in vehicles. Acerta is extending its product line to also offer analytics for_x000D_
batteries. To extend the product line to batteries, Acerta needs to tune its algorithms and products to provide_x000D_
meaningful estimates of the quality of a battery system at the end of life. This requires answering the following_x000D_
questions (among others): (1) what is the state of the charge, (2) what is the health of the cells, and (3) what is_x000D_
the expected state of degradation over the next years. Since Acerta uses machine learning mixed with domain_x000D_
knowledge, it needs domain expertise on feature engineering (i.e., determining what sensor values should be_x000D_
provided) and feature processing (i.e., use features to extract information gain)., program purpose french : This Engage project aims to establish a new collaboration between Acerta Analytics Solutions and the research_x000D_
team supported by Dr. Michael Fowler. The two parties have not engaged before in any form of collaboration_x000D_
to date. Advances in computing technology enable new forms of data analysis. Acerta offers analytics_x000D_
solutions to identify warranty issues of manufactured goods at the end of life. This means that Acerta's_x000D_
algorithms analyze data collected from the manufactured good before it leaves the factory and based on the data_x000D_
determines whether the good should be shipped to the customer or whether it will likely have a warranty issue_x000D_
and thus should be inspected in the factory prior to shipping. Currently, Acerta is applied to products using_x000D_
gearboxes, engines, and bearings in vehicles. Acerta is extending its product line to also offer analytics for_x000D_
batteries. To extend the product line to batteries, Acerta needs to tune its algorithms and products to provide_x000D_
meaningful estimates of the quality of a battery system at the end of life. This requires answering the following_x000D_
questions (among others): (1) what is the state of the charge, (2) what is the health of the cells, and (3) what is_x000D_
the expected state of degradation over the next years. Since Acerta uses machine learning mixed with domain_x000D_
knowledge, it needs domain expertise on feature engineering (i.e., determining what sensor values should be_x000D_
provided) and feature processing (i.e., use features to extract information gain)., agreement title english : Artificial Intelligence Based Assessment of LiIon Battery Failure, agreement title french : Artificial Intelligence Based Assessment of LiIon Battery Failure, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-22, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada