Structural Health Monitoring (SHM) is increasingly regarded as a key enabling technology for future composite aircraft, not only for maintenance optimization but also for a possible re-interpretation of structural sizing constraints. In civil aviation, SHM is generally associated with condition-based maintenance and reduced downtime. However, its real strategic value may be larger when composite airframes are considered, since certification-driven safety margins are strongly influenced by the limited detectability of barely visible damage and hidden internal flaws. In this work, the aircraft-level value of SHM is revisited with stronger attention to the monitoring rationale, the integration burden, and the system implications of certification-aware structural design. Starting from an A220-like reference aircraft, a multidisciplinary analysis is used to quantify how permanently installed SHM sensors affect operating empty weight, mission fuel, field performance, and direct operating costs. Three configurations are compared by varying sensor density and safety-margin assumptions. The results show that SHM becomes especially attractive when it is not treated only as a maintenance add-on, but as a design enabler capable of partially relaxing conservative composite sizing assumptions. Under the investigated assumptions, a reduced structural safety margin combined with a limited sensor density leads to nearly unchanged aircraft performance together with lower cash operating costs and lower block fuel. The study highlights that the system-level effectiveness of SHM depends on the balance between sensing coverage, installation mass, and the certification credit granted to the monitoring capability.

Remaining Useful Life (RUL) prediction for turbofan engines is critical for balancing operational safety against maintenance costs and environmental impact from premature replacements. Models must reliably extrapolate beyond training data, yet no single method performs optimally across all operational contexts, making model selection fundamentally heuristic. Rather than demonstrating individual model superiority, this work recognizes that reliable prognostic performance emerges from adaptive model contribution. This work introduces an ensemble framework integrating three components: (1) legacy-representative data splitting that mirrors realistic deployment where models predict for newer assets using historical data, (2) median absolute deviation-based outlier filtering for stability, and (3) WTA³ weighting that dynamically adjusts model influence based on cycle-by-cycle performance. This treats model coordination as a time-varying optimization problem adapting to evolving degradation patterns. The framework is validated on NASA CMAPSS data using six diverse models spanning traditional machine learning (Random Forest, XGBoost, Support Vector Regression) and deep learning (LSTM, CNN, Transformer). We investigate how prediction robustness changes during temporal extrapolation, whether adaptive weighting provides more stable forecasts than individual models or fixed combinations, and how uncertainty quantification supports safer maintenance decisions. Initial validation demonstrates that the WTA³ meta-ensemble achieves approximately 3 cycles RMSE and under 3 cycles MAE, representing over 30% improvement over the best individual model, with particularly strong performance in the critical late-life phase. The ensemble maintains highly stable predictions with well-calibrated confidence intervals and substantially improved coverage compared to individual models. This work reframes prognostics from competitive model selection to adaptive coordination, demonstrating that ensemble stability under extrapolation can be systematically achieved. The approach provides actionable confidence bounds for aerospace maintenance programs, enabling cost-efficient scheduling while reducing environmental waste, directly supporting both economic and sustainability objectives where data scarcity and safety criticality are paramount.

In October 2026 the British Institute of Non-destructive Testing (BINDT) SHM working group will be holding a one-day event to gather the SHM communities view on how to help accelerate the adoption of SHM technologies. The workshop will gathered information from the wider SHM community with respect to 3 distinct topics:

a) what are the features of existing successful SHM technology applications

b) what existing standards, guidance and reference documents exist for SHM technology

c) what are the gaps and what can be done to fill them.

In this talk a summary of the intended long-term objectives of the work and discussion during the workshop will be presented to delegates. EWSHM delegates will be informed how they can contribute by adding data to the wider open survey on the subject that is being performed.

Structural Health Monitoring Systems (SHMS) offer significant potential to enhance safety, reliability, and cost efficiency in aerospace structures by enabling continuous or on-demand automated inspections without the need for human intervention. Despite extensive research on diagnostic and prognostic algorithms over the past three decades, the practical adoption of SHMS in the aerospace sector remains limited. One major barrier is the scarcity of systematic methodologies and quantitative case studies that clearly demonstrate the economic trade-offs, costs, and benefits associated with SHMS implementation.

This study presents a comprehensive cost–benefit analysis of a fibre optic–based SHMS integrated into a composite helicopter rotor blade. The analysis considers both the economic gains—such as maintenance manpower savings and improved inspection efficiency—and the potential cost penalties, including increased repair complexity and higher spare part costs due to embedded sensors.

The rotor blade life cycle is simulated using a Probabilistic Damage Tolerance Analysis (PDTA) model developed in MATLAB. The PDTA approach enables an hour-by-hour simulation of component degradation and repair events, calculating the Average Probability per Flight Hour (APFH) in compliance with Federal Aviation Administration (FAA) airworthiness requirements, as it is presented in Figure 1.

To capture the broader maintenance and operational context, a detailed cost model was developed following the IDEF0 functional modelling methodology, which systematically maps each process activity along with its inputs, outputs, constraints, and required resources. This cost model is fully integrated within the PDTA simulation environment. The optimal Life Cycle Management (LCM) strategy is determined using an Ant Colony Optimization (ACO) algorithm. In this framework, the optimization problem is expressed through parameter vectors representing different PDTA variables and maintenance decision parameters. Two scenarios are evaluated according to the Life Cycle Costing (LCC) methodology: (i) the baseline “as-is” configuration without SHMS, and (ii) the “with SHMS” configuration incorporating sensor-based monitoring and decision support.

Simulation results indicate that, for comparable blade reliability levels (i.e., similar APFH values), the introduction of the SHMS yields a measurable economic benefit. This benefit is particularly evident when the SHMS is paired with optimized maintenance strategies derived from the ACO-based LCM analysis. Notably, even in applications where SHMS implementation might initially appear marginal—such as rotor blades that are relatively easy to inspect manually—a positive net economic outcome can still be achieved through an integrated, model-driven approach to life cycle management.

Overall, this work provides a structured methodology and quantitative evidence to support the cost-effectiveness of SHMS in aerospace applications. By coupling probabilistic damage modelling with process-level cost analysis and optimization, it contributes to bridging the gap between technological feasibility and economic viability, thereby promoting a more informed and data-driven adoption of SHMS in rotorcraft structures