Quantum mechanics

Visual inspection

Engineering

Reliability engineering

Downtime

Materials science

Lubricant

Classifier (UML)

Power (physics)

Computer science

Grease

Condition monitoring

Mechanical engineering

Physics

Simulation

Automotive engineering

Reliability (semiconductor)

Artificial intelligence

Composite material

Turbine

Artificial neural network

Bearing (navigation)

Electrical engineering

Computers in Industry

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.

Hybrid physics-informed neural networks for main bearing fatigue prognosis with visual grease inspection