One Platform for Digital MRO

As aircraft and defense systems become more connected and digitalized, the need for OEMs to better manage sustainment costs increases. Extensive amounts of data generated by commercial fleets, flight operations, maintenance costs and non-conformities must be captured and elevated to knowledge and know-how to continuously optimize fleets. Effective maintenance can positively impact asset availability to prevent technical delays and optimize maintenance stops, ensuring timely asset delivery.

Leveraging flight operation data from an aircraft fleet, elevating the raw data and enriching the virtual twin of the fleet with it will enable key analysis and provide a clear understanding of what needs to be changed on the aircraft and when to optimize all resources — manpower, supply chain, explicit instructions and tools. This requires detailed preparation and good knowledge-driven collaboration between the different industrial players. Digital MRO, with the ability to leverage any data —  pilot feedback, sensor data, images and more — is elevated into actionable knowledge by all parties, making this possible. Discover here how Dassault Aviation leveraged this.

Data is collected from different aircraft systems, sensor data and flight operation data. But it also comes from maintenance and pilot tech logs from maintenance information systems, pilot feedback, technicians, images and more. Data can even come from the supply chain. Clearly, these diverse systems contain different kinds of data (structured/unstructured) that need to be aggregated, merged and harmonized. This information is managed, analyzed and transformed into actionable knowledge and know-how to make better-informed decisions.

The data collected generates insights that can be leveraged and elevated directly on the platform using the virtual twin experience as a vehicle. Machine learning improves event and issue analysis. Artificial intelligence can analyze complex events (identify similarities, trends, facts, key expressions and taxonomies). This helps to better understand new incoming data to improve predictive maintenance.

The abundance of data coming from the aircraft and its systems provides the whole industry with an unprecedented opportunity to monitor various parameters continuously, a significant improvement from inspections spread over specific time intervals. Across aviation, there has been a move towards condition-based maintenance and integrated aircraft health management. Predictive maintenance is also used to improve the design of new aircraft and boost innovation. Thus, maintenance challenges could even be transformed into benefits to boost fleet availability.

Support and services use model-based systems engineering to simulate operations scenarios. Event logbooks, flight data and maintenance data inputs run simulations for support and services to anticipate potential issues in the expected scenario. Simulating the cost impact will help to improve the maintenance plan and optimize aircraft systems designs.