Simulation-to-real Unsupervised Domain Adaptation Gear Diagnostics Based on Entropy
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https://orcid.org/0009-0000-5935-2201
Rui Zhu
Toby Verwimp
Alexandre Maurício
Hao Wen
Seyed Ali Hosseinli
Konstantinos Gryllias
Abstract
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.
How to Cite
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unsupervised domain adaptation, gear diagnostics, phenomenological model, transfer learning, pitting
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