Developing reliable health indicators (HIs) for aeronautical composite structures is challenging because damage evolution is complex, stochastic, and affected by uncertainties such as impact events and manufactured defects. In this context, passive and active structural health monitoring (SHM) techniques provide complementary information: acoustic emission (AE) captures temporally dense signatures of damage activity, whereas guided waves (GW) provide state-sensitive interrogation of the structure at discrete inspection times. This work therefore investigates heterogeneous sensor fusion for HI construction in composite T-stiffener panels subjected to run-to-failure compression-compression fatigue loading. Modality-specific AE- and GW-based HI frameworks are first developed independently using signal processing and AI. The resulting HIs are then synchronized in the fatigue-cycle domain and combined through inter-modality fusion to obtain a single fused HI. The study addresses key challenges including the absence of true HI labels, synchronization of passive and active measurements, and learning from limited multimodal overlap. The results show the potential of combining AE and GW to construct more informative and robust HIs for prognostics of composite aerostructures.
Morteza Moradi, D. Zarouchas· e-Journal of Nondestructive...· 0 citations
Ensuring reliability, safety, and economic efficiency in airline operations requires maintenance and fleet scheduling strategies that explicitly account for uncertainty in Remaining Useful Life (RUL) predictions. However, the integration of prognostic uncertainty into operational decision-making remains a major challenge. In practice, tail assignment (TA) and maintenance scheduling (MS) are typically optimized separately or sequentially, thereby limiting the effective use of predictive health information despite their strong interdependencies. This paper proposes a unified optimisation framework that jointly integrates TA, MS, and predictive maintenance (PdM) under RUL with confidence intervals. The problem is formulated as a stochastic mixed-integer linear program, and a scalable solution approach is developed by embedding a neural network surrogate to approximate expected disruption costs resulting from RUL uncertainty. The proposed framework is evaluated using operational scenarios derived from real-world airline data. Results show that explicitly incorporating prognostic uncertainty in a joint planning model reduces operational risk, i.e., downstream disruption costs and flight cancellations, compared to deterministic and sequential approaches, at the expense of moderate increases in planning cost. These findings highlight the value of tightly coupling predictive maintenance with operational planning and demonstrate the potential of surrogate-assisted stochastic optimisation for scalable, uncertainty-aware airline decision-making.
Benno Käslin, Marta Ribeiro, D. Zarouchas et al.· 0 citations
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