The procedure of damage accumulation in composites, especially during fatigue loading, is a complex phenomenon of stochastic nature which depends on a number of parameters such as type and frequency of loading, stacking sequence, material properties, and so on. Toward condition-based health monitoring and decision making, the need for not only diagnostic but also prognostic tools rises and draws increasing attention in the last few years. To this direction, we model the damage evolution in composites as a doubly stochastic hidden Markov process that manifests itself via structural health monitoring observations, that is, acoustic emission data. The damage process is modeled via an extension of the classic hidden Markov models to account for nonhomogeneity, that is, age dependence in state transitions. The observations come from acoustic emission data recorded throughout fatigue testing of open-hole carbon–epoxy coupons. A procedure that utilizes multiple observation sequences from a training dataset and estimates in a maximum likelihood sense the optimal model parameters is presented and applied in unseen data via a cross-validation rationale. Diagnostics of the most likely health state determination, average degradation level, and prognostics of the remaining useful life are among the capabilities of the presented stochastic model.

Condition-Based Maintenance (CBM) is a policy that uses information about the health condition of systems and structures to identify optimal maintenance interventions over time, increasing the efficiency of maintenance operations. Despite CBM being a well-established concept in academic research, the practical uptake in aviation needs to catch up to expectations. This research aims to identify challenges, limitations, solution directions, and policy implications related to adopting CBM in aviation. We use a generalizable and holistic assessment framework to achieve this aim, following a process-oriented view of CBM development as an aircraft lifecycle management policy. Based on various inputs from industry and academia, we identified several major sets of challenges and suggested three primary solution categories. These address data quantity and quality, CBM implementation, and the integration of CBM with future technologies, highlighting future research and practice directions.

The development of health indicators (HI) of diagnostic and prognostic potential from generally uninformative raw sensor data is both a challenge and an essential feature for data-driven diagnostics and prognostics of composite structures. In this study, new damage-sensitive features, developed from strains acquired with Fiber Bragg Grating (FBG) and acoustic emission (AE) data, were investigated for their suitability as HIs. Two original fatigue test campaigns (constant and variable amplitude) were conducted on single-stringer composite panels using appropriate sensors. After an initial damage introduction in the form of either impact damage or artificial disbond, the panels were subjected to constant and variable amplitude compression–compression fatigue tests. Strain sensing using FBGs and AE was employed to monitor the damage growth, which was further verified by phased array ultrasound. Several FBGs were incorporated in special SMARTapes^TM, which were bonded along the stiffener’s feet to measure the strain field, whereas the AE sensors were strategically placed on the panels’ skin to record the acoustic emission activity. HIs were developed from FBG and AE raw data with promising behaviors for health monitoring of composite structures during service. A correlation with actual damage was attempted by leveraging the measurements from a phased array camera at several time instances throughout the experiments. The developed HIs displayed highly monotonic behaviors while damage accumulated on the composite panel, with moderate prognosability.