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作 者:Jianfeng Zhu Chunli Wang Chuankun Li Xinjiang Gao Jinsong Zhao
机构地区:[1]Sinopec Research Institute of Safety Engineering, Qingdao 266071, China [2]State Key Laboratory of Chemical Engineering Department of Chemical Engineering Tsinghua University, Beijing 100084, China
出 处:《Chinese Journal of Chemical Engineering》2016年第7期881-885,共5页中国化学工程学报(英文版)
基 金:Supported by the National High Technology Research and Development Program of China(2013AA040701)
摘 要:Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper,we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper, we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local inter- dependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.
关 键 词:Dynamic alarm predictionAlarm managementThe n-gram modelAlarm sequence
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