Dahl Zacharias, Hämäläinen Aleksanteri, Karhinen Aku, Miettinen Jesse, Böhme Andre, Lillqvist Samuel, Haikonen Sampo, Viitala Raine
Department of Mechanical Engineering, Aalto University, Espoo, Finland.
Kongsberg Maritime AS, Borgundveien 340, 6009 Ålesund, Norway.
Data Brief. 2024 Dec 2;57:111171. doi: 10.1016/j.dib.2024.111171. eCollection 2024 Dec.
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.
通过深度学习进行准确的系统健康状态预测需要大量多样的数据。然而，现实世界中数据稀缺对开发强大的故障诊断模型构成了挑战。本研究引入了两个广泛的数据集，即阿尔托模拟器数据集和阿尔托齿轮故障数据集，这些数据集是在受控实验室条件下收集的，旨在推进基于深度学习的故障诊断。这些数据集涵盖了广泛的齿轮故障，包括合成和实际故障模式，在配备多个传感器的小型方位推进器试验台上进行了复制。数据具有不同运行条件下的各种故障类型和严重程度。所收集的全面数据，以及创建合成故障和复制常见齿轮故障的方法，为开发和测试智能故障诊断模型提供了宝贵资源，增强了它们在不同场景下的泛化能力和鲁棒性。
