在生物和膜处理系统中对多变量统计过程监测的整体评估
Kathryn B Newhart1, Molly C Klanderman2, Amanda S Hering2
1United States Military Academy, West Point, New York 10996, United States.
概括
这项研究增强了使用自适应动态主要组件分析 (AD-PCA) 早期检测系统故障的市政废水处理厂 (WWTP) 的无监督过程监测. 该方法成功地在违反现有的操作值之前发现了各种故障.
科学领域:
- 环境工程 环境工程
- 过程控制 过程控制
- 数据科学数据科学数据科学
背景情况:
- 在市政废水处理厂 (WWTP) 检测故障的无监督过程监控是具有挑战性的,因为复杂的,大容量的传感器数据.
- 现有的方法往往无法及时检测微妙或多变量故障.
研究的目的:
- 广泛测试和调整一个无监督的过程监控方法,用于在完全分散的WWTP中早期检测故障.
- 评估自适应动态主要组件分析 (AD-PCA) 在识别各种故障类型的有效性.
主要方法:
- 使用了自适应动态主要组件分析 (AD-PCA),一种修改后的尺寸缩小技术.
- 数据是按处理过程和操作状态的子集;模型使用为期一周的训练窗口和中等累积方差.
- 设置了高的故障检测值,以确保及时识别偏差.
主要成果:
- 实施的AD-PCA方法成功检测到尖端故障,单变量漂移故障和多变量转移故障.
- 在现有的操作值之前发现了故障,证明了早期检测能力的提高.
- 该系统在实时监控复杂的多变量流程方面表现出有效性.
结论:
- AD-PCA是一种有前途的无监督方法,用于快速检测WWTP中的故障.
- 需要进一步的研究来完善异常值的去除和检测多变量漂移故障,以加强实时监控.
- 优化的AD-PCA可以提高废水处理过程的运行稳定性和效率.
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