GE Aerospace has highlighted how airlines can make better use of the vast amounts of data already generated by their aircraft to improve reliability, reduce operational disruption and identify potential maintenance problems before they lead to an AOG.
Speaking at the Predictive Aircraft Maintenance (PAM) APAC 2026 conference in Singapore today, Frank Siegers, head of commercial, APAC & China at GE Aerospace Software as a Service, described an airline operational environment in which maintenance teams must contend with challenges ranging from supply chain disruption and ageing aircraft to regulatory requirements, technology integration and the loss of experienced workforce knowledge.
“Our industry’s operational environment is incredibly complex,” Siegers told delegates.
However, his presentation argued that airlines do not necessarily need to look far for the technology capable of tackling some of those challenges.
“There are tools available today – we just need to harness what’s available to make a tangible difference for the industry,” he said.
A central theme was breaking down data silos and turning information collected during everyday operations into actionable maintenance intelligence.
GE Aerospace’s Event Measurement System (EMS), for example, processes full-flight data and can provide visibility across maintenance, engineering, safety, fuel and flight operations.
Siegers illustrated the potential through several real-world examples of data moving from detection to maintenance intervention.
In one recent case, GE Aerospace EMS identified abnormal bleed pressure on an aircraft’s right-hand engine. "Subsequent trend analysis identified degradation of the pressure regulating and shut-off valve (PRSOV), which was replaced five days later," Siegers explained.
According to the presentation, the intervention returned the trend to normal and averted the potential for a dual-bleed failure.
Another example demonstrated the value of looking beyond an individual aircraft. After multiple operators reported GPS failures at a major domestic airport, cross-fleet and geospatial analysis helped trace the problem not to the aircraft themselves, but to a defective GPS tracker fitted to a catering truck that was producing localised jamming signals.
The examples reinforced a wider message from the session: predictive maintenance is increasingly about connecting existing sources of operational data and using analytics and AI to spot patterns that may otherwise remain hidden.
Siegers said a shared dataset can also "serve multiple operational priorities simultaneously, allowing safety, maintenance and fuel-efficiency teams to work from the same information rather than separate data silos."
