Abstract:

To address the strong subjectivity of prior data in dynamic Bayesian network (DBN) for dynamic risk assessment, this study focuses on a coal⁃slurry preparation system and uses a long short⁃term memory recurrent neural network (LSTM⁃RNN) to optimize the prior data of the DBN. Through its gating mechanism and cell state, the LSTM⁃RNN dynamically screens and retains key information, effectively captures early⁃stage features in long sequences, filters redundant noise, and robustly learns long⁃term dependencies. By learning the time‑series historical operational performance data of the system, the prior data of the DBN is optimized. Bidirectional inference is then performed on the optimized DBN to achieve dynamic reliability analysis of the coal⁃slurry preparation system. The results show that forward inference yields the optimized trend of system reliability, whereas backward inference identifies key events and weak links before and after optimization.

Key words: Bayesian estimation, Monte Carlo simulation, Dynamic Bayesian network, Long short?term memory recurrent neural network, Reliability

摘要：

为解决动态风险评估中动态贝叶斯网络（DBN）先验数据主观性强的问题，以煤浆制备系统为研究对象，利用长短时记忆循环神经网络（LSTM⁃RNN）模型优化了DBN的先验数据；LSTM⁃RNN通过门控机制与细胞状态，动态筛选和保留关键信息，有效记忆长序列中的早期特征，同时过滤冗余噪声，稳定学习长时间跨度的依赖关系，通过对系统历史运行性能时序数据进行学习，实现对DBN的先验数据优化；对优化后煤浆制备系统的DBN进行双向推理，实现对煤浆制备系统的动态可靠性分析。结果表明，对煤浆制备系统DBN进行正向推理，可得到优化后的系统可靠性变化趋势；进行反向推理，可得到优化前后的关键事件及薄弱环节。

关键词: 贝叶斯估计, 蒙特卡洛模拟, 动态贝叶斯网络, 长短时记忆循环神经网络, 可靠性