rotating-machinery

Python library to download public bearing and rotating-machinery fault datasets (CWRU, Paderborn, XJTU-SY, FEMTO, IMS, …) from their original sources and convert them into one Parquet format, with the same columns and labels for every dataset.

Physics-guided deep learning for bearing fault diagnosis (100% accuracy on XJTU Gearbox). Learnable adaptive moment features replace hand-crafted statistics. PyTorch · 5-class vibration classification · BPFO/BPFI/BSF fault detection · reproducible research pipeline.

OSHWA-certified open-hardware vibration sensing node for measurement integrity, machine-condition monitoring and Gateway-side telemetry experiments.

Automated multi-channel sensor data analysis pipeline for rotating component test engineering.

End-to-end multi-sensor fault diagnosis & evidence-ranking pipeline for rotating machinery (vibration, thermal, current, torque).

Rushton turbine stirred-tank CFD in STAR-CCM+: steady MRF and transient sliding mesh validated against the published Power Number (Po 4.85 / 4.89 vs 5.0), after a dimensional audit traced a 2.1× discrepancy to a blade placement defect.