[1]崔铁军,李莎莎.联系数和属性多边形的系统故障模式识别[J].智能系统学报,2022,17(3):568-575.[doi:10.11992/tis.202011019]
CUI Tiejun,LI Shasha.System fault pattern recognition based on the connection number and attribute polygon[J].CAAI Transactions on Intelligent Systems,2022,17(3):568-575.[doi:10.11992/tis.202011019]
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CUI Tiejun,LI Shasha.System fault pattern recognition based on the connection number and attribute polygon[J].CAAI Transactions on Intelligent Systems,2022,17(3):568-575.[doi:10.11992/tis.202011019]
联系数和属性多边形的系统故障模式识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第3期
页码:
568-575
栏目:
学术论文—智能系统
出版日期:
2022-05-05
- Title:
- System fault pattern recognition based on the connection number and attribute polygon
- Keywords:
- safety system engineering; set pair analysis; connection number; attribute polygon; multi factor influence; system fault; maximum membership; pattern recognition
- 分类号:
- TP18; X913
- 摘要:
- 为综合考虑多因素影响下根据系统故障标准模式对系统故障样本模式进行识别，定义了属性多边形并提出基于联系数和属性多边形的系统故障样本模式识别方法。首先，建立了故障模式识别系统，利用特征函数确定单因素的故障模式联系度，确定属性多边形结构。其次，利用属性多边形的同异反分量面积确定多因素联合影响的故障模式联系度。最终，根据故障标准模式的最大隶属原则对故障样本模式进行识别。使用简单电气系统作为实例，实施了识别方法，得到多因素联合影响下的各故障样本模式的识别结果。实验结果表明，通过两阶段联系度的计算可综合考虑单因素和多因素影响下的故障模式特征，进而识别系统故障样本模式。
- Abstract:
- To identify the system fault sample patterns under the influence of multiple factors, an attribute polygon is defined, and the system fault sample pattern recognition method is proposed based on the connection number and the attribute polygon. First, the fault pattern recognition system is established, the connection degree of a single factor is determined by the characteristic function, and the structure of the attribute polygon is determined. Second, the area with an identical difference, contrary to the attribute polygon, is used to determine the fault pattern connection degree affected by multiple factors. Finally, the fault sample pattern is identified according to the maximum membership principle of the fault standard pattern. Taking a simple electrical system as an example, the recognition method is implemented, thereby obtaining the recognition results of each fault sample pattern synergistically influenced by multiple factors. The results show that via the two-stage connection degree calculation, the fault pattern characteristics can be comprehensively considered under the influence of a single factor and multiple factors, thereby identifying the system fault sample pattern.
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备注/Memo
收稿日期:2020-11-19。
基金项目:国家自然科学基金项目(52004120)；辽宁省教育厅科学研究经费项目(LJ2020QNL018)；辽宁省教育厅基本科研项目(LJKQZ2021157)；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，博士，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及空间故障网络理论。主持国家自然科学基金项目1项。获得优秀论文奖十余项。出版学术专著4部，获发明专利22项。发表学术论文100余篇;李莎莎，讲师，博士，主要研究方向为安全管理及其智能分析。参与因素空间和空间故障树理论的研究。主持国家自然科学基金项目1项。出版学术专著2部，获发明专利5项。发表学术论文20余篇
通讯作者:崔铁军.E-mail：ctj.159@163.com
更新日期/Last Update:
1900-01-01