A performance degradation prediction approach for turbo-generator bearing considering complex working conditions based on clustering indicator and self-optimized deep learning model

Abstract

The reliability of turbo-generator bearings affects the stability and safety of power plants. To detect early symptoms of failure, prediction of the performance degradation of turbo-generator bearings is particularly important. At present, most related research relies on accelerated degradation test platforms; however, the industrial field environment and complex working conditions may affect practical application. In this study, a performance degradation prediction approach for turbo-generator bearings considering complex working conditions based on a clustering indicator and a self-optimized deep learning model (SODLM) is proposed. First, the Dirichlet process-Gaussian mixture clustering model is introduced to construct the performance degradation indicator (PDI) of the turbo-generator. Then through introducing the hyper-parameter constraint, the hyper-parameter influence significance comparison mechanism, and the minimum training epoch, an improved hyperband (IH) is established to realize the automatic tuning of the hyper-parameters in the prediction model. Furthermore, the SODLM is constructed based on the IH and a stacked one-dimensional convolutional neural network. Finally, the performance degradation trend of a turbo-generator bearing in a practical industrial field is successfully predicted through the proposed approach. The experimental analysis demonstrates that the proposed PDI avoids the influence of sampling frequency and complex working conditions. Compared with traditional optimization algorithms and prediction models, the proposed performance degradation prediction approach demonstrates better accuracy and stability.