[1]崔铁军,李莎莎.多因素集对分析的系统故障模式识别方法[J].智能系统学报,2022,17(2):387-392.[doi:10.11992/tis.202011006]
CUI Tiejun,LI Shasha.System fault-pattern recognition based on set pair analysis with multiple factors[J].CAAI Transactions on Intelligent Systems,2022,17(2):387-392.[doi:10.11992/tis.202011006]
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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第2期
页码:
387-392
栏目:
学术论文—人工智能基础
出版日期:
2022-03-05
- Title:
-
System fault-pattern recognition based on set pair analysis with multiple factors
- 作者:
-
崔铁军1, 李莎莎2
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1. 辽宁工程技术大学 安全科学与工程学院,辽宁 葫芦岛 125105;
2. 辽宁工程技术大学 工商管理学院,辽宁 葫芦岛 125105
- Author(s):
-
CUI Tiejun1, LI Shasha2
-
1. College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China;
2. School of Business Administration, Liaoning Technical University, Huludao 125105, China
-
- 关键词:
-
智能科学; 安全系统工程; 空间故障树理论; 多因素影响; 集对分析; 故障模式; 识别方法; 联系度和识别度
- Keywords:
-
intelligent science; safety system engineering; space fault tree theory; multi factor influence; set pair analysis; fault pattern; recognition method; connection degree and recognition degree
- 分类号:
-
TP18; X913; C931.1
- DOI:
-
10.11992/tis.202011006
- 摘要:
-
为研究多因素影响下系统故障模式识别,根据已有故障标准模式对故障样本模式进行分析,提出基于集对分析联系数和故障分布的系统故障模式识别新方法。根据故障背景建立故障模式识别系统,分析故障样本模式与故障标准模式,确定联系度各联系分量,计算联系度和识别度,最后通过确定故障样本模式与故障标准模式关系完成识别。对某电气系统实例分析给出了方法流程,获得了模式识别结果,从而为有针对性的采取预防和治理措施提供了决策支持。
- Abstract:
-
To study system fault-pattern recognition under the influence of multiple factors, a sample fault pattern was analyzed according to the existing standard fault patterns. A system fault-pattern recognition method based on the set pair analysis connection number and fault distribution is proposed. On the basis of the fault data, we developed the fault-pattern recognition system, analyzed the fault sample and standard fault patterns, determined the relating components and their connection degrees, and calculated the connection and recognition degrees. Finally, the relationship between fault sample and standard fault patterns was determined to complete the identification. An example based on an electrical system analysis showed the process of the method by achieving efficient pattern recognition and proving its effectiveness. This study provides targeted decision-making support for taking preventive and governance measures.
备注/Memo
收稿日期:2020-11-04。
基金项目:国家自然科学基金项目(52004120);辽宁省教育厅基本科研项目(LJKQZ2021157);辽宁省教育厅科学研究经费项目(LJ2020QNL018);辽宁工程技术大学学科创新团队项目(LNTU20TD-31)
作者简介:崔铁军,副教授,主要研究方向为系统可靠性及系统故障智能分析理论。提出和建立了空间故障树理论及空间故障网络理论。获得多个省及协会科技奖励,获发明专利25项,发表学术论文200余篇,出版学术专著6部;李莎莎,副教授,主要研究方向为安全管理及其智能分析方法。参加了因素空间和空间故障树理论的研究。获发明专利5项,发表学术论文20余篇,出版学术专著3部
通讯作者:崔铁军.E-mail:ctj.159@163.com
更新日期/Last Update:
1900-01-01