Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach

The bearing temperature forecasting provide can provide early detection of the gearbox operating status of wind turbines. To achieve high precision and reliable performance in bearing temperature forecasting, a novel hybrid model is proposed in the paper, which is composed of three phases. Firstly, the variational mode decomposition (VMD) method is employed to decompose raw bearing temperature data into several sub-series with different frequencies. Then, the SAE-GMDH method is utilized as the predictor in the subseries. The stacked autoencoder (SAE) is for the low-latitude features of raw data, while the group method of data handling (GMDH) is applied for the sub-series forecasting. Finally, the imperialist competitive algorithm (ICA) optimizes the weights for subseries and combines them to achieve the final forecasting results. By analytical investigation and comparing the final prediction results in all experiments, it can be summarized that (1) the proposed model has achieved excellent prediction outcome by integrating optimization algorithms with predictors; (2) the experiment results proved that the proposed model outperformed other selective models, with higher accuracies in all datasets, including three state-of-the-art models.

Fatigue Life Prediction for Gas Turbine Bearing Under 3D Elastoplastic Mixed Lubrication Condition

Gas turbine bearings usually work in harsh lubrication environments, with typical characteristics such as high speed, high temperature, and heavy load. Severe working conditions bring micro-contacts in local areas between the bearing rolling elements and the rings. Stress concentration and plastic deformation may further occur between the contact pairs, which will reduce the fatigue life of the bearing. In order to predict bearing fatigue life accurately, in this study, the effects of the real surface roughness and plastic deformation of the bearing are considered, a three-dimensional(3D) elastoplastic mixed lubrication model for gas turbine bearing is established, the 3D dynamic elastoplastic stress calculations under the surface are conducted, and the relative fatigue life of the bearing is predicted. Based on the 3D elastoplastic mixed lubrication model for the bearing, the influences of speed, load, roughness and material hardening on lubrication performance and fatigue life are also studied, which would provide theoretical guidance for the lubrication design and life prediction of the gas turbine bearings.