Automated Aircraft Maintenance Data Processing and Analytics

Research Area(s): Internet of Things, Systems Integration | Funder: NSERC | Program: ARD
PI Name: Mihai Albu | Faculty/Department: Faculty of Applied Sciences & Technology

The industry partner for this NSERC ARD Grant is MHIRJ Aviation Group Heavy Industries - Regional Jet, formerly Bombardier Inc. Regional Jet). MHIRJ provides comprehensive critical operational, engineering and customer support solutions for the global regional aircraft industry. MHIRJ needs applied research assistance from Humber College to address two complementary maintenance-related needs. The first need is for Automated Maintenance Data Processing system that will work with maintenance data submitted - in multiple formats and over varying intervals - by aircraft operators (airline companies). This data will be ingested, processed, analyzed, and outputted as databases for further analytics of the individual performance of aircraft in the individual fleets; as well as how the overall performance of individual fleets compare to an aggregated and anonymized view of the fleets of competing operators. The proposed solution can reduce significantly the time and effort required by MHIRJ personnel to administer and maintain the process. The automated data processing is not time critical as long as the output is available within 24 hours of data submission. The second need is for an Integrated Analytical Tool to assist ground maintenance crews in their critical and time-sensitive 'dispatchability' decision: 'go,' 'go with limitation,' and 'no go,' in a very narrow window of time between flights, based on real-time operational data from each flight. The design of the Integrated Analytic Tool will be based upon the same data processing and analytics algorithms developed for Automated Maintenance Data Processing. This will result in more consistent data interpretation, improved efficiency by aircraft maintenance crews, faster turn-around times of aircraft with improved safety. Humber College's applied research team will provide relevant expertise in textual data mining, language processing, machine learning, data analytics, and database design, to assist in developing software algorithms and tools necessary for the intended solutions. The project will improve the competitiveness for MIHRJ and establish a stronger collaboration between MHIRJ and Humber College.