College of Safety and Ocean Engineering, State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China; Department of Chemical Engineering, University of California, Davis, CA 95616, USA.
Department of Chemical Engineering, University of California, Davis, CA 95616, USA.
ISA Trans. 2019 Feb;85:274-283. doi: 10.1016/j.isatra.2018.10.032. Epub 2018 Oct 30.
Industrial alarm systems play an essential role for the safe management of process operations. With the increase in automation and instrumentation of modern process plants, the number of alarms that the operators manage has also increased significantly. The operators are expected to make critical decisions in the presence of flooding alarms, poorly configured and maintained alarms and many nuisance alarms. In this environment, if the incoming alarms can be correctly predicted before they actually occur, the operators may have a chance to address and possibly avoid abnormal behaviors by taking corrective actions in time. Inspired by the application of deep learning in natural language processing, this paper presents an alarm prediction method based on word embedding and recurrent neural networks to predict the next alarm in a process setting. This represents both a novel approach to alarm management as well as a novel application of natural language processing and deep learning techniques to this problem. The proposed method is applied to an actual case study to demonstrate its performance.
工业报警系统对过程操作的安全管理起着至关重要的作用。随着现代过程工厂自动化和仪器仪表的增加，操作人员需要管理的报警数量也显著增加。操作人员需要在存在大量报警、配置和维护不当的报警以及大量误报警的情况下做出关键决策。在这种环境下，如果能够在实际发生之前正确预测即将到来的报警，操作人员就有可能通过及时采取纠正措施来解决问题，并有可能避免异常行为。受深度学习在自然语言处理中的应用启发，本文提出了一种基于词嵌入和循环神经网络的报警预测方法，用于预测过程环境中的下一个报警。这既代表了一种新的报警管理方法，也代表了自然语言处理和深度学习技术在该问题上的新应用。该方法应用于实际案例研究，以展示其性能。
