基于人工免疫系统的石油化工过程故障 诊断应用研究
Research on application of artificial immune system on petrochemical process fault diagnosis
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- 引用格式:
-
汤大可.基于人工免疫系统的石油化工过程故障 诊断应用研究[J].天然气与石油,2022,40(2):131-135.doi:10.3969/j.issn.1006-5539.2022.01.020
TANG Dake.Research on application of artificial immune system on petrochemical process fault diagnosis[J].Natural Gas and Oil,2022,40(2):131-135.doi:10.3969/j.issn.1006-5539.2022.01.020
- DOI:
- 10.3969/j.issn.1006-5539.2022.01.020
- 作者:
- 汤大可
TANG Dake
- 作者单位:
- 中国石油天然气股份有限公司广东石化分公司, 广东 揭阳 515200
PetroChina Guangdong Petrochemical Branch, Jieyang, Guangdong, 515200, China
- 关键词:
- 人工免疫系统;石油化工过程;工艺安全;故障诊断
Artificial immune system; Petrochemical processes; Process safety; Fault diagnosis
- 摘要:
- 石油化工过程故障诊断是保障石油化工过程工艺安全的一项重要技术。人工免疫系统作为一种新兴的人工智能方法,具备识别非我信号的能力以及自适应和自学习能力等特点,近年来被应用到了石油化工过程故障诊断领域。为进一步提升人工免疫系统在石油化工过程故障诊断方面的应用效果,对人工免疫网络算法、否定选择算法和克隆选择算法这三种基于人工免疫系统的石油化工过程故障诊断方法进行了综述,并分别探讨了这三种方法在石油化工过程故障诊断中的应用。最后提出了人工免疫系统应用于石油化工过程故障诊断还需解决的问题,以期为今后同类研究的深入开展提供借鉴。
Petrochemical process fault diagnosis method is an important technology to ensure the process safety of petrochemical processes. As an emerging artificial intelligence method, artificial immune system has the ability to identify non-self signals as well as possessing strong adaptive and self-learning capabilities. In recent years, it has been introduced into the field of petrochemical process fault diagnosis. In order to further improve the application effect of artificial immune system on petrochemical process fault diagnosis, this paper reviewed three main fault diagnosis methods based on artificial immune system: artificial immune network algorithm, negative selection algorithm, and clone selection algorithm. On this basis, applications of these three fault diagnosis methods on petrochemical process fault diagnosis are reviewed and discussed. In the end, this paper put forward problems which need to be solved to apply artificial immune system in petrochemical process fault diagnosis, in order to provide reference for the in-depth development of similar research in the future.
