Aalto Gear Fault datasets for deep-learning based diagnosis.

Abstract

Accurate system health state prediction through deep learning requires extensive and varied data. However, real-world data scarcity poses a challenge for developing robust fault diagnosis models. This study introduces two extensive datasets, Aalto Shim Dataset and Aalto Gear Fault Dataset, collected under controlled laboratory conditions, aimed at advancing deep learning-based fault diagnosis. The datasets encompass a wide range of gear faults, including synthetic and realistic failure modes, replicated on a downsized azimuth thruster testbench equipped with multiple sensors. The data features various fault types and severities under different operating conditions. The comprehensive data collected, along with the methodologies for creating synthetic faults and replicating common gear failures, provide valuable resources for developing and testing intelligent fault diagnosis models, enhancing their generalization and robustness across diverse scenarios.