Grant: Engage Grants Program

The Mercedes-Benz Fuel Cell Division (MBFC) in Burnaby, Canada develops and runs the manufacturingprocesses required for the assembly of Fuel Cell Stack prototypes. MBFC uses the Manufacturing ExecutionSystem (MES) to collect and analyze data from the manufacturing lines to the database system. The size of thecollected data is very high and MBFC is not able to detect certain fuel cell defects in a timely manner.This Engage grant project aims to develop and evaluate a mechanism that reduces the time taken to search theMercedes-Benz Fuel Cell (MBFC's) big data and detect a failure in the manufacturing process in a real-timefashion. This will be done by critically studying and understanding MBFC's database schema and analysing agraph version of this database. A dimensionality reduction and feature extraction mechanism will be developedto search a specific cluster of the MBFC big data set and identify defective fuel cells using the Gas DiffusionLayer (GDL) bleed through method. Finally, a mechanism will be proposed to detect defective fuel cells in atotal data dump scenario.The proposed research is innovative because it will devise a novel real-time big data feature extractiontechnique that combines a low latency and high efficiency dimensionality reduction technique with a clusteringgraph-based database mechanism. Potential pitfalls and risks will be identified, mitigated and managed throughenhanced research methodologies, using diverse dimensionality reduction models and providing alternativeapproaches to detect features in big data.MBFC will be involved in the project by providing technical support, consultation and advice on issuesrelated to their database schema, Manufacturing Execution Systems (MES) interface and their GDL bleedthrough testing processes. This research will enable MBFC to establish a presence in fuel cell manufacturingand help establish Canada as a global leader in innovative green technologies.