Composite bone replicas are widely used in biomechanical testing as alternatives to cadaveric specimens, with numerical models often complementing or replacing experiments. The reliability of these models depends strongly on accurate material parameters. This study investigates a fourth-generation Sawbones composite L5 vertebra, updating cortical material properties under isotropic and transversely isotropic modelling assumptions. Finite element models were calibrated using free-free experimental modal analysis, revealing differences between manufacturer-provided material properties and the measured specimen behaviour. For both models, matching the specimen mass required reducing the cortical density from 1.64 g/cm³ to 1.423 g/cm³. In the isotropic model, the Young’s modulus was reduced from 16,000 MPa to 6500 MPa. In the transversely isotropic model, longitudinal and transverse Young’s moduli were reduced from 16,000 MPa and 11,000 MPa to 6400 MPa and 5500 MPa, respectively, while the shear moduli decreased from 4370 MPa and 6350 MPa to 3500 MPa and 2540 MPa. In both models, the Poisson’s ratio was increased from 0.26 to 0.30. These updates reduced the average eigenfrequency error to 6.12% (isotropic) and 5.83% (transversely isotropic), with the first five modes errors reduced to 3.10% and 2.80%, respectively, substantially improving numerical representation of L5 vertebral mechanics. The updated vertebral FE model and accompanying workflow enhance the reliability of future FE analyses, improve interpretation of Sawbones vertebra biomechanical results, and support vibration-based biomechanical applications such as implant fixation assessment.

This paper outlines the motivation for the research, reviewed the relevant SOTA in TL and CM, and identified some current research gaps. Moreover a dedicated test rig that will be used for methodological development and experimental validation has been described in detail. Finally, a structured research plan has been proposed, with the ultimate objective of developing a robust and scalable methodology combining ML and DTs for fault diagnostics of WT gearboxes, thereby contributing meaningfully to the field of PHM.

Gearbox failures are a major contributor to the levelized cost of electricity of wind turbines, owing to their long repair time and large share of total turbine downtime. Condition monitoring enables early fault detection, reducing unplanned downtime and the associated economic impact, while digital twins provide virtual representations of physical assets that support advanced diagnostics. This work couples the two: a phenomenological gear vibration model generates a synthetic dataset spanning healthy operation and several gear pitting severities, and healthy measurements from the real asset are used to estimate a transfer function, through cepstrum liftering, that is applied to the synthetic signals so that they better reproduce the experimental response. By stochastically varying the model parameters within prescribed ranges, a labeled synthetic dataset is produced and used to train a deep learning unsupervised domain adaptation (UDA)networkthatcouples adversarial training with entropy-based sample weighting and an information maximization loss, reducing the mismatch between simulated and measured data and improving diagnostic robustness on real measurements. The framework is validated on vibration data from an in-house back-to-back, single-stage helical gear test rig, in which pitting was artificially introduced on the tooth flank. Against state-of-the-art (SOTA) adversarial methods and a source-only baseline, the proposed approach more accurately distinguishes healthy operation from multiple pitting severity levels, supporting its potential for diagnosis on real applications.