Fault early warning of equipment in nuclear power plant can effectively reduce unplanned forced shutdown and avoid significant safety accidents. This paper presents a Bayesian Long Short-Term Memory (LSTM) neural network method for fault early warning method of nuclear power turbine. The Long Short-Term Memory neural network prediction model is developed to address data uncertainty while taking into account complicated situation of the equipment operation. Quantitative reliability validation method is established based on Bayesian inference. A wavelet packet multi-scale time-frequency analysis is employed for data denoising. A Probabilistic Principal Component Analysis (PPCA) method combined with key factor analysis is proposed for dimension reduction and dealing with the data uncertainty. The principal component inverse search method is developed to identify the critical factors mainly contributing to the turbine fault. Numerical results indicate that the proposed novel model is validated with Bayesian confidence of 92% by using the real-world steam turbine data and the model can provide accurate warning in the early creep stage of the fault.

Aiming at solving the problems of small fault data samples and insufficient remaining useful life (RUL) prediction accuracy of nuclear power machinery, a method based on an exponential degradation model is proposed to predict the RUL of equipment after the failure warning system alarm. After data preprocessing, time-domain feature extraction, selection, and dimensionality reduction fusion of multiple degradation variables, the exponential degradation model is constructed based on the Bayesian process, and prior information is used. As an application, the RUL of a nuclear power turbine was calculated based on actual monitoring data, the precision curve was used to evaluate the prediction effect, and the RUL prediction results verified the effectiveness of the proposed method.