This article reports on anomaly detection performance of data-driven models based on a few selected autoencoder topologies and compares them to the performance of a set of popular classical vibration-based condition indicators. The evaluation of these models employed data that consisted of baseline gearbox runs and the associated runs with seeded bending cracks in the root of the gear teeth for eight different gear pairings. The analyses showed that the data-driven models, trained on a subset of baseline data, outperformed classical condition indicators as anomaly detectors and may show promise for damage assessment.
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Autoencoders, Machine learning, Neural Networks, Gears, Gear crack, Anomaly detection, Condition indicators
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