A Hybrid Model for Wind Turbine Main Bearing Fatigue with Uncertainty in Grease Observations

Abstract

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.