Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine

Abstract

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.