Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine
Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine
复制标题
核电汽轮机故障预警贝叶斯长短期记忆模型
DOI：
10.1109/access.2020.2980244
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
You, Dongdong
中科院分区：
文献类型：
--
作者：
Liu, Gaojun;Gu, Haixia;You, Dongdong
核电站设备故障预警可以有效减少非计划强制停堆，避免重大安全事故的发生。提出了一种用于核电涡轮机故障预警的贝叶斯长短期记忆（LSTM）神经网络方法。为了解决数据的不确定性，同时考虑到设备运行的复杂情况，开发了长短期记忆神经网络预测模型。建立了基于贝叶斯推理的定量可靠性验证方法。采用小波包多尺度时频分析对数据进行去噪。提出了一种结合关键因子分析的概率主成分分析（PPCA）方法，用于降维和处理数据的不确定性。采用主元逆搜索法对引起涡轮机故障的关键因素进行识别。数值结果表明，利用实际蒸汽涡轮机数据，所提出的新模型的贝叶斯置信度为92%，该模型可以在故障的早期蠕变阶段提供准确的预警。涡轮机数据。
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.