The principal component inverse search method is developed to identify the critical factors mainly contributing to the turbine fault. A Probabilistic Principal Component Analysis (PPCA) method combined with key factor analysis is proposed for dimension reduction and dealing with the data uncertainty. A wavelet packet multi-scale time-frequency analysis is employed for data denoising. Quantitative reliability validation method is established based on Bayesian inference. 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. This paper presents a Bayesian Long Short-Term Memory (LSTM) neural network method for fault early warning method of nuclear power turbine. Fault early warning of equipment in nuclear power plant can effectively reduce unplanned forced shutdown and avoid significant safety accidents.
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