Available historical field data shows that wind turbine main bearing failure can lead to major operation and maintenance costs due to unscheduled downtime. For legacy turbines, fa- tigue is one of the major failure modes and, to a degree, can be partially modeled with physics-based formulations. Unfor- tunately, existing bearing fatigue models can potentially be inaccurate due to lack of understanding of the lubricant degra- dation. One way to enhance these models is to track the grease damage along with the bearing fatigue damage. However, the need of grease degradation data can become an impedi- ment for such strategy. In this paper, we will demonstrate that it is possible to calibrate grease degradation models with cost-efficient periodic visual inspections. Knowing that such inspections introduce observation uncertainty to the model, we will use a hybrid physics-informed deep neural networks to quantify such uncertainties within our models. We built a hybrid model that fuses the physics-based understanding of the bearing fatigue failure with the ability of data-driven layers to compensate the missing physics, with respect to the grease degradation. The proposed hybrid model is also ca- pable of decoding uncertain visual grease inspections with a custom designed classifier. We illustrate the merits of the model with the support of case studies, where we test inspec- tion with different levels of conservatism to train the model and compare the predictions of these models on an artificial wind park. Results from the case studies indicate the success- ful prognostic performance of the trained with limited and noisy observations. While grease damage is predicted with 0.3% root mean square error as a result of baseline inspection campaign, bearing life is prediction is conservatively off only by months for aggressive turbines that have 10 years of life.
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Physics-informed neural networks, Wind energy, Uncertainty quantification
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