This paper introduces an expert system approach for predicting the remaining useful life (RUL) of light aircraft structural components by analyzing operational and maintenance records. The expert system consists of four modules: knowledge acquisition, knowledge base, inference, and explanation. The knowledge acquisition module retrieves data from mandatory records, such as aircraft logbooks and mass and balance sheets. The knowledge base stores specific remaining useful lives (SRULs) for different load profiles that are determined using numerical strength analysis. The inference module utilizes the Palmgren-Miner rule to estimate the accumulated fatigue damage of the structural component based on the input data and the knowledge base. Lastly, the explanation module links the accumulated damage to the maintenance program and suggests the appropriate maintenance action. The Cessna 172R main landing gear leg is utilized as a case study, demonstrating the variance of RUL depending on the operating conditions. The objective of this approach is to enhance light aircraft maintenance decision making and advance operational safety.

Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid physics–data framework to quantify operational-load-driven uncertainty in the main landing gear strut of a Cessna 172. High-fidelity finite-element strain–life simulations were combined with a quadratic Ridge surrogate and a two-layer bootstrap to generate full probabilistic RUL distributions. The surrogate mapped five mass-and-balance inputs (fuel, front seats, rear seats, forward and aft baggage) to per-flight fatigue damage with high accuracy (R² = 0.991 ± 0.013). At the same time, ±3% epistemic confidence bands were attached via resampling. Borgonovo’s moment-independent Δ indices were applied to incremental damage (ΔD) in this context, revealing front-seat mass as the dominant driver of fatigue variability (Δ = 0.502), followed by fuel (0.212), rear seats (0.199), forward baggage (0.141), and aft baggage (0.100). The resulting RUL distribution spanned 9 × 10⁴ to >2 × 10⁶ cycles, with a fleet average of 0.41 million cycles (95% CI: 0.300–0.530 million). These results demonstrate that operational levers—crew assignment, fuel loading, and baggage placement—can significantly extend strut life. Although demonstrated on a specific training fleet dataset, the methodological framework is, in principle, transferable to other aircraft or mission types. However, this would require developing a new, component-specific finite element model and retraining the surrogate using a representative set of mass and balance records from the target fleet.

Under EU mandates, sustainable aviation fuel (SAF) reaches airports blended into Jet A-1, yet engine data for such commercial fuel are rare. This exploratory study compared it with fatty acid methyl ester (FAME) blends in a JJ-1400 microturbojet. Both campaigns used the same Jet A-1 reference, and all fuels contained 5% v/v turbine oil. The first tested 25% and 50% FAME at 60,000–120,000 rpm. The second tested 20–100% of a commercial Jet A-1 containing up to 50% (m/m) renewable component at 50,000–120,000 rpm. Fuel effects on thrust and exhaust gas temperature (EGT) at fixed shaft speed were estimated with an additive two-way model. The thrust effects of the SAF-containing mixtures ranged from −0.3% to +1.4% (90% confidence limits −3.2% to +4.4%) and were not statistically significant. With 25% and 50% FAME, thrust was 5.3% and 12.5% lower and EGT about 50 and 70 K higher than with Jet A-1, more than in other small gas turbines at fixed speed. Consistency checks made bench drift an unlikely cause, but the fixed test order prevented a full separation from changes during the session. The reference runs agreed within 0.7%, whereas a similarity relation predicted 6–10% less thrust on the warmer 2024 day.