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Hybrid physics-informed neural networks for main bearing fatigue prognosis with visual grease inspection
Computers in Industry ( IF 10.0 ) Pub Date : 2021-01-03 , DOI: 10.1016/j.compind.2020.103386
Yigit A. Yucesan , Felipe A.C. Viana

Field failures of wind turbine main bearings yield to undesired downtime and significant maintenance costs. Fatigue failure is a dominant mode for legacy turbines, which can be expressed with physics-informed models to some extent. However, these models often inherent large uncertainties due to unknown lubricant degradation mechanism. Therefore, periodical assessment of the grease state plays a crucial role in calibration of bearing fatigue models. As opposed to detailed laboratory analysis, grease visual inspection can lead to large uncertainties in characterization of grease condition (although visual inspection can be cost and time effective). In this paper, we introduce a hybrid model for main bearing fatigue damage accumulation and calibrated using only visual grease inspections. In our hybrid model, bearing fatigue damage portion consists of known physics formulations, and unknown grease degradation is represented with deep neural networks. In addition, we introduce a custom tailored classifier that enables the model to map from damage scale to visual rankings. Results showed that the bearing fatigue prognosis model can be successfully calibrated, even with limited and noisy field observations. Moreover, the model can help optimizing park reliability by suggesting turbine-specific regreasing intervals. The source codes and links to the data can be found in the following GitHub repository https://github.com/PML-UCF/pinn_wind_bearing.



中文翻译:

混合物理信息神经网络用于主轴承疲劳预测和视觉润滑脂检查

风力涡轮机主轴承的现场故障导致不希望的停机时间和大量的维护成本。疲劳失效是传统涡轮机的主要模式,可以在一定程度上用物理模型来表示。然而,由于未知的润滑剂降解机理,这些模型通常固有的较大不确定性。因此,定期评估润滑脂状态在校准轴承疲劳模型中起着至关重要的作用。与详细的实验室分析相反,油脂外观检查可能会导致油脂状况表征方面存在较大的不确定性(尽管外观检查可能既节省成本又节省时间)。在本文中,我们介绍了一种用于主轴承疲劳损伤累积的混合模型,并且仅使用目视润滑脂检查即可进行校准。在我们的混合模型中 轴承疲劳损伤部分由已知的物理公式组成,未知的油脂降解由深层神经网络表示。另外,我们引入了定制的定制分类器,该分类器使模型能够从损害等级映射到视觉等级。结果表明,即使在有限且嘈杂的现场观察中,轴承疲劳预测模型也可以成功地进行校准。此外,该模型可以通过建议特定于涡轮的重新润滑间隔来帮助优化停车位可靠性。可以在以下GitHub存储库https://github.com/PML-UCF/pinn_wind_bearing中找到源代码和数据链接。我们引入了定制的定制分类器,该分类器使模型能够从损坏程度映射到视觉等级。结果表明,即使在有限且嘈杂的现场观察中,轴承疲劳预测模型也可以成功地进行校准。此外,该模型可以通过建议特定于涡轮的重新润滑间隔来帮助优化停车位可靠性。可以在以下GitHub存储库https://github.com/PML-UCF/pinn_wind_bearing中找到源代码和数据链接。我们引入了定制的定制分类器,该分类器使模型能够从损坏程度映射到视觉等级。结果表明,即使在有限且嘈杂的现场观察中,轴承疲劳预测模型也可以成功地进行校准。此外,该模型可以通过建议特定于涡轮的重新润滑间隔来帮助优化停车位可靠性。可以在以下GitHub存储库https://github.com/PML-UCF/pinn_wind_bearing中找到源代码和数据链接。

更新日期:2021-01-03
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