Reference number : GC-2017-Q4-00035, amendment number : , agreement type : G, recipient legal name : Al-Anbagi, Irfan (University of Regina), recipient country : CA, recipient province : nan, recipient city : Saskatchewan, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The Mercedes-Benz Fuel Cell Division (MBFC) in Burnaby, Canada develops and runs the manufacturing_x000D_
processes required for the assembly of Fuel Cell Stack prototypes. MBFC uses the Manufacturing Execution_x000D_
System (MES) to collect and analyze data from the manufacturing lines to the database system. The size of the_x000D_
collected data is very high and MBFC is not able to detect certain fuel cell defects in a timely manner._x000D_
This Engage grant project aims to develop and evaluate a mechanism that reduces the time taken to search the_x000D_
Mercedes-Benz Fuel Cell (MBFC's) big data and detect a failure in the manufacturing process in a real-time_x000D_
fashion. This will be done by critically studying and understanding MBFC's database schema and analysing a_x000D_
graph version of this database. A dimensionality reduction and feature extraction mechanism will be developed_x000D_
to search a specific cluster of the MBFC big data set and identify defective fuel cells using the Gas Diffusion_x000D_
Layer (GDL) bleed through method. Finally, a mechanism will be proposed to detect defective fuel cells in a_x000D_
total data dump scenario._x000D_
The proposed research is innovative because it will devise a novel real-time big data feature extraction_x000D_
technique that combines a low latency and high efficiency dimensionality reduction technique with a clustering_x000D_
graph-based database mechanism. Potential pitfalls and risks will be identified, mitigated and managed through_x000D_
enhanced research methodologies, using diverse dimensionality reduction models and providing alternative_x000D_
approaches to detect features in big data._x000D_
MBFC will be involved in the project by providing technical support, consultation and advice on issues_x000D_
related to their database schema, Manufacturing Execution Systems (MES) interface and their GDL bleed_x000D_
through testing processes. This research will enable MBFC to establish a presence in fuel cell manufacturing_x000D_
and help establish Canada as a global leader in innovative green technologies., program purpose french : The Mercedes-Benz Fuel Cell Division (MBFC) in Burnaby, Canada develops and runs the manufacturing_x000D_
processes required for the assembly of Fuel Cell Stack prototypes. MBFC uses the Manufacturing Execution_x000D_
System (MES) to collect and analyze data from the manufacturing lines to the database system. The size of the_x000D_
collected data is very high and MBFC is not able to detect certain fuel cell defects in a timely manner._x000D_
This Engage grant project aims to develop and evaluate a mechanism that reduces the time taken to search the_x000D_
Mercedes-Benz Fuel Cell (MBFC's) big data and detect a failure in the manufacturing process in a real-time_x000D_
fashion. This will be done by critically studying and understanding MBFC's database schema and analysing a_x000D_
graph version of this database. A dimensionality reduction and feature extraction mechanism will be developed_x000D_
to search a specific cluster of the MBFC big data set and identify defective fuel cells using the Gas Diffusion_x000D_
Layer (GDL) bleed through method. Finally, a mechanism will be proposed to detect defective fuel cells in a_x000D_
total data dump scenario._x000D_
The proposed research is innovative because it will devise a novel real-time big data feature extraction_x000D_
technique that combines a low latency and high efficiency dimensionality reduction technique with a clustering_x000D_
graph-based database mechanism. Potential pitfalls and risks will be identified, mitigated and managed through_x000D_
enhanced research methodologies, using diverse dimensionality reduction models and providing alternative_x000D_
approaches to detect features in big data._x000D_
MBFC will be involved in the project by providing technical support, consultation and advice on issues_x000D_
related to their database schema, Manufacturing Execution Systems (MES) interface and their GDL bleed_x000D_
through testing processes. This research will enable MBFC to establish a presence in fuel cell manufacturing_x000D_
and help establish Canada as a global leader in innovative green technologies., agreement title english : Clustering and dimensionality reduction based techniques for low latency big data feature extraction applications, agreement title french : Clustering and dimensionality reduction based techniques for low latency big data feature extraction applications, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, 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