The subsea production system is a vital equipment for offshore oil and gas production. The control system is one of the most important parts of it. Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal. However, subsea sensors degrade rapidly due to harsh working environments and long service time. This leads to frequent false alarm incidents. A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed. A combinatorial algorithm is proposed to group sensors. The long short-term memory network (LSTM) is used to establish a single inference model. A counting-based judging method is proposed to identify abnormal sensors. Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method. The results show that the proposed method can identify the abnormal sensors effectively.

中文翻译：

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基于组合推理的海底生产控制系统异常传感器识别方法

海底生产系统是海上油气生产的重要设备。控制系统是其中最重要的部分之一。采集和处理海底传感器的信号是判断海底生产控制系统是否正常的唯一方法。然而，由于恶劣的工作环境和较长的使用时间，海底传感器的性能退化很快。这导致误报事件频繁发生。提出一种基于组合推理的海底生产控制系统异常传感器识别方法。提出了一种组合算法来对传感器进行分组。长短期记忆网络（LSTM）用于建立单一推理模型。提出了一种基于计数的判断方法来识别异常传感器。来自南海海上平台的现场数据被用来证明该方法的效果。结果表明，该方法能够有效识别异常传感器。