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一种通过协会规则挖掘和相关性分析的初始警报检测方法
1School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
这项研究引入了一种用于检测首发警报的新方法,即序列中的初始警报. 这种方法有助于通过识别和过工业系统中随后的相关报警来减少报警过载.
科学领域:
- 工业自动化和控制系统
- 数据挖掘和机器学习应用程序.
背景情况:
- 复杂的工业过程依赖于实时报警系统进行操作监控.
- 报警系统经常因相关或冗余的报警而过载.
- 识别第一个警报对于减轻警报洪水和理解异常传播至关重要.
研究的目的:
- 提出和验证一种新的方法,用于首次发出警报的检测.
- 为了解决相关警报场景中第一出警报识别研究的稀缺性.
- 减少警报过载,提高工业监控系统的效率.
主要方法:
- 使用关联规则挖掘,特别是FP-Growth算法和J-Measure,从历史数据中提取警报关系.
- 开发一种初出警报确定策略,采用相关性分析和对条件概率的假设测试.
- 实施首发规则选标准以识别和整合冗余规则,确保可靠的结果.
主要成果:
- 拟议的方法有效地从历史警报序列中提取警报关联规则.
- 通过统计分析,通过统计分析,可以明确区分第一个和随后的相关报警.
- 开发的选标准成功过了多余的规则,提供了整合的首发警报识别.
结论:
- 基于关联规则挖掘和相关性分析的新首发警报检测方法是有效的.
- 这种方法在复杂的工业环境中在管理报警超载时提供了显著的改进.
- 这些发现有助于研究不足的第一个警报检测领域,提高系统可靠性.
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