Fault detection in wastewater treatment plants using distributed PCA methods

A. Sánchez-Fernández, M. J. Fuente, G. Palmero
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引用次数: 21

Abstract

This paper proposes a distributed fault detection and diagnosis method based on Principal Component Analysis (PCA) in a whole plant monitoring scheme. The method is based on the decomposition of the plant into multiple blocks using plant topology. A local PCA based fault detection method is applied in each block and the results are sent to the central node to fuse the information and to detect and diagnose faults in the global plant. This method is compared with the centralized PCA method and some distributed principal component analysis (DPCA) methods in a wastewater treatment plant (WWTP). The objective is to check which of the distributed methods implemented is the best one in terms of detecting faults and minimizing the communication cost between the blocks. Empirical results on the WWTP show that the DPCA method based on local models has very good results.
基于分布式主成分分析方法的污水处理厂故障检测
提出了一种基于主成分分析(PCA)的全厂分布式故障检测与诊断方法。该方法基于使用植物拓扑将植物分解为多个块。在每个块中应用基于局部主成分分析的故障检测方法,并将结果发送到中心节点进行信息融合,对全局设备进行故障检测和诊断。将该方法与集中式主成分分析方法和一些分布式主成分分析方法在某污水处理厂进行了比较。目标是检查哪一种实现的分布式方法在检测故障和最小化块之间的通信开销方面是最好的。污水处理厂的实证结果表明,基于局部模型的DPCA方法具有很好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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