Turbine Generator Condition Monitoring Using Bayesian-estimated Multi-output Gaussian process

Performing online condition monitoring for turbine generators is challenging but imperative to avoid unexpected failure and reduce maintenance costs. To achieve long-term signal prediction, this paper proposes a Bayesian-estimated multi-output Gaussian process (MOGP) framework. In the proposed framework, the mean function captures the global trend caused by degradation, and MOGP models the residual term between the mean function and the observed value which captures the local variation caused by measurement noise and environmental factors. The covariance function of MOGP characterizes the temporal and cross-correlation between observations of signals simultaneously to achieve precise prediction. Moreover, the parameters of the MOGP are learned via Bayesian estimation to avoid overfitting issues. Then, the signals are fused into a 1-D health index (HI) which assesses the health status of turbine generators. Finally, the proposed method is applied to two real turbine generators, and the result compared with both statistical and deep learning-based methods demonstrates its superiority.