The aviation industry is undergoing a digital revolution, with predictive maintenance, artificial intelligence, and digital twins reshaping aircraft operations. With 35 years of experience, Pete Boeskov, Chief Engineer for Sim Engineering and Analytics at Boeing Global Services, has been pivotal in advancing Boeing's digital capabilities, helping airlines transition from reactive maintenance strategies to data driven, predictive solutions.

We sat down with Boeskov and discussed how Boeing is leveraging simulation technologies, Al, and fleet-wide analytics to redefine strategic maintenance planning, enhance operational efficiency, and reduce unplanned downtime. 

Evolution of predictive maintenance

Aircraft maintenance has heavily relied on scheduled inspections for decades with airlines following timelines for inspections and component replacements. “The current, but now four-decade-old, scheduled maintenance process recognises the inherent reliability of systems and components but still requires physical examinations by a mechanic at the aircraft,” explains Boeskov. "Maintenance plans are built around time—you do an inspection of a component at 3,000 hours, for example.”

With advancements in sensor technology, and fleet-wide data analytics, Boeing along with partner airlines and the FAA, is now moving toward a data-driven maintenance model that reduces reliance on manual inspections. “With two decades of data sharing and analysis, we are paving the way to a new model—one we are calling condition-based scheduled maintenance (CBSM),” Boeskov says.

At the core of CBSM is Integrated Aircraft Health Management (IAHM), which is a continuous monitoring of an aircraft’s health through onboard sensors. These sensors collect critical operational data to identify current condition, optimise component replacement timing, and alert operators to necessary maintenance actions before an issue occures.

“This regulator-approved solution represents a groundbreaking evolution to enable operators to replace manual inspections with data-driven decisions and reducing maintenance burden,” Boeskov highlights. Al and data analytics 

Al is transforming aircraft maintenance, moving beyond simple analytics to practical applications that improve decision making, enhance efficiency, and reduce costs. Boeing is at the forefront of this shift, integrating Al-driven predictive maintenance tools to identify potential faults before they impact operations.

“We want Al to be impactful beyond analytics. It needs to be applied in practical ways, such as our autonomous drone inspections that we demonstrated with USAF C-5s," says Boeskov. Al’s capabilities extend far beyond inspections. Boeing is also using Natural Language Processing (NLP) to analyse logbook data, structuring vast amounts of maintenance records by component, condition, location, and required action.

"Right now, we're developing a way to infuse documentation and task management with Al-generated summaries that combine it all concisely into the work package for the mechanic,” Boeskov explains. This ensures that maintenance crews receive clear, data-driven instructions, reducing the time spent on diagnostics and allowing them to focus on solving issues efficiently. A key part of Boeing’s strategy is its AHM digital solution, which has been in the market for over 20 years.

“AHM provides real-time monitoring, diagnostics and prognostic insights about aircraft systems," Boeskov notes. When integrated with Boeing’s Maintenance Toolbox, airlines gain access to a fully connected maintenance ecosystem that optimises aircraft availability.

By analysing vast amounts of data from aircraft systems, Boeing can identify patterns and anomalies that indicate potential failures, allowing for proactive maintenance instead of reactive fixes. This also includes insights into fleet positioning and spare parts logistics, ensuring that aircraft spend more time in the air and less time on the ground.

“Our objective is to use the broad range of data to uncover the root causes and move unscheduled maintenance to scheduled,” Boeskov says. 

Simulation and digital twins

As predictive maintenance evolves, simulation and digital twin technology are critical in improving maintenance planning and optimising operations. Boeing is now leveraging advanced simulation models to test predictive maintenance strategies before airlines implement them in operations.

One area where simulation is proving invaluable is maintenance cost and inventory optimisation. Boeing runs simulations to analyse the operational impacts of prognostic maintenance adoption to help airlines understand the trade-offs between maintenance expenses, parts inventory and operational efficiency.

“We analyse how carriers dealing with dirty or clogged heat exchangers can benefit from a prognostic approach. Our simulations help understand the trade offs, revealing how these factors differ among operators worldwide," Boeskov explains. Beyond traditional simulation digital twins are now shaping the future of aircraft maintenance. These data-driven models replicate the behaviour of individual aircraft, systems, and even specific components, enabling precise maintenance predictions.

"Digital twins allow us to understand the individual aircraft, individual system, even the individual component. We now have such a high degree of detail in the data that we can tailor predictions and knowledge about that component," Boeskov highlights. “This enables our predictive maintenance insights to become individualised, not just at the fleet level, but down to the specific aircraft," he adds. However, achieving this level of accuracy requires a steady flow of data from aircraft throughout the lifecycle.

"We tend to think of a digital twin as a 3D virtual image. In reality, a digital twin is a lot of data in a database that allows us to understand the lifetime experiences of a specific component to make more accurate predictions," Boeskov explains.

Boeing is also working closely with airlines to validate and refine digital twin models, ensuring that predictive insights translate into real-world efficiency gains. By integrating Al, loT, and sensor data, digital twins are enabling a smarter approach to fleet management.

“Simulation can help build customer confidence in the underlying data by testing the effects of proactive maintenance decisions and showcasing optimal strategies and trade-offs," Boeskov notes. As more airlines embrace digital twins and predictive maintenance, Boeing expects a significant shift in how fleet management and maintenance strategies are executed. 

Bridging commercial and government aviation

While predictive maintenance and digital analytics are revolutionising commercial aviation, Boeing is applying these innovations to government and defence operations. Although both sectors share the same technologies, their priorities and operational goals differ, requiring customised solutions.

For commercial airlines, the focus is on efficiency, cost reduction, and maximising fleet availability. Airlines aim to optimise maintenance planning, reduce delays, and enhance passenger experiences. “In commercial aviation, there is an emphasis on moving people and cargo with efficiency," Boeskov says.

“Our predictive maintenance tools help airlines minimise maintenance events and optimise fleet readiness.”

On the other hand, government and defence customers have a different priority: mission readiness. Aircraft must be available at a moment's notice, often operating in remote or unpredictable environments where maintenance resources are limited. Boeskov explains. “Predictive analytics in this space are designed to enhance aircraft availability, ensuring that fleets are ready for deployment when needed.” 

Future of digital aviation

As aviation continues to embrace digital transformation, Boeing is at the forefront of advancing Al, predictive maintenance, and fleet management technologies.

With these technologies becoming the new standard in aviation, Boeing is leading the charge toward a more data-driven, efficient, and proactive maintenance ecosystem. As airlines and MROs embrace these innovations, the industry is set to enter a new era of operational efficiency and reliability, WB CBSM Boeing’s model of condition based scheduled maintenance ff We want Al to be impactful beyond analytics. It needs to be applied in practical ways, such as our autonomous drone inspections that we demonstrated with USAF C-5s J J if Digital twins allow us to understand the individual aircraft, individual system, even the individual component. We now have such a high degree of detail in the data that we can tailor predictions and knowledge about that component J J A Boeing is working closely with airlines to validate digital twin models

One area where simulation is proving invaluable is maintenance cost A The Boeing 737 aircraft.