EXSYN contributed to the following article by AEROSPACE TechReview, published 27th March 2020:
Predictive Maintenance Analytics: Smarter, Safer & More Efficient Operations By Charlotte Daniels
Predictive maintenance has progressed from industry buzzword into a goal for many operators. Today, several airlines and MROs are demonstrating how to use data to increase fleet reliability. But how are they able to fully benefit from the vast wealth of information available, and mine it effectively without incurring unmanageable costs?
Data comes in many forms and from various sources in an airline – the vast amount available today created the term ‘big data’. Unless robust digital solutions are installed that can aggregate, distribute and analyse information, data is useless. Complex algorithms are required for this analysis, specifically machine-learning algorithms to handle aircraft and engine sensor information.
According to an Oliver Wyman MRO Survey, the global fleet of commercial aircraft could generate 98 million terabytes of data per year by 2026, due to big data. Aircraft data comes from sources including the flight data recorder (FDR), engine health monitoring (EHM) and airframe health monitoring (AHM); each receiving and transmitting thousands of parameters from in-built sensors, often down to component level. The amount of data has implications for transmission costs and for an airline’s connectivity and storage capabilities. That is, for the data to perform proactively, it needs to feed data regularly into maintenance (M&E) and operational systems to create a current picture. Having the infrastructure for this can feel cost-prohibitive for carriers.