在水系统中用于异常检测和预测性维护的K-最近邻居
João Pablo Santos da Silva1, André Laurindo Maitelli1
1Computer Engineering and Automation Department, Federal University of Rio Grande do Norte, 3000 Senador Salgado Filho Avenue, Natal 59078-970, RN, Brazil.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究使用传感器数据识别了水系统中的液压异常. k-最近邻近算法准确地预测了关闭,改善了供水维护.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 维护对于水源质量和供应可靠性至关重要.
- 水管腐蚀导致漏水和降低水质.
- 识别系统中的液压异常是必不可少的.
研究的目的:
- 为了识别水系统中的液压异常.
- 使用传感器数据开发状态的预测模型.
- 为了确定最重要的传感器参数,以便准确预测.
主要方法:
- 从各种管道特征的供水网络收集的数据.
- 在液压系统的不同位置安装传感器计量器.
- 使用k-最近邻居 (KNN) 机器学习算法进行预测.
主要成果:
- 开发了一个模型,将传感器参数与状态相关联.
- 确定了用于预测关闭的关键传感器参数.
- 通过KNN算法实现了使用关闭的准确预测.
结论:
- 该KNN算法有效预测供水系统中的状态.
- 传感器数据分析可以识别液压异常并帮助维护.
- 具有最小参数的优化模型提供了准确的预测.
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