Wind turbine blade bearing fault detection with Bayesian and Adaptive Kalman Augmented Lagrangian Algorithm

Abstract

As a critically supporting and rotational component for wind turbines, blade bearings need special health monitoring for safe operation in actual industrial conditions. One of the main difficulties of the wind turbine blade bearing condition monitoring is noisy signals generated under fluctuating slow speed with heavy loads. This is because blade bearing rotation speed is influenced by blade flipping and external disturbances, and this influence is time-varying. This paper proposes a new method, Bayesian and Adapted Kalman Augmented Lagrangian (BAKAL), to filter the signal under this time-varying condition. The new method uses a two-step search (coarse and fine search) to deal with the filtering process based on Bayesian Augmented Lagrangian (BAL) framework. In addition, both linear and nonlinear effects and their sparsity are considered for model construction. Finally, the smearing problem in the frequency spectrum is dealt with through signal resample in the order domain for superior performance of fault diagnosis. The proposed BAKAL algorithm is strictly validated in several experiments under approximately fixed speed and variable speed within the condition of heavy loadings. The experiments use an industrial and rotational wind turbine blade bearing with natural defects, which has been served in an actual wind power plant for over 15 years. The experimental results demonstrate the effectiveness of the proposed method.