Racquel Knust Domingues

Racquel Knust Domingues is a PhD candidate in Mechanical Engineering at the Federal University of Santa Catarina (UFSC), Brazil, where she conducts research in vibration-based condition monitoring and predictive maintenance for rotating machinery. Her work focuses on bearing fault detection and diagnosis using self-supervised and semi-supervised learning, aiming to exploit large-scale unlabeled vibration data. She is also interested in physics-enhanced machine learning to increase the reliability and interpretability of diagnostic models.

She holds an MSc in Mechanical Engineering from UFSC, where she investigated Time-Synchronous Averaging (TSA) to separate deterministic and random components in vibration signals and support bearing fault detection, and developed vibration-based rotational speed estimation (a “virtual tachometer”) for time-synchronous resampling.

At present, she is a visiting researcher with the Data, Vibration and Uncertainty (DVU) Group at the University of Cambridge, carrying out research under the supervision of Prof. Alice Cicirello. Her doctoral research includes collaboration with Dynamox S.A., a Brazilian company specialized in industrial condition monitoring and vibration-based asset health solutions.

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