基于RBF-NLPCA模型的空气质量监测网络多传感器故障检测与隔离

M. Harkat, Y. Tharrault, G. Mourot, J. Ragot
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引用次数: 20

摘要

提出了一种基于非线性主成分分析的数据驱动传感器多故障检测与隔离方法。将主曲线算法与两个三层径向基函数(RBF)网络相结合,得到了RBF- nlpca模型。提出了非线性情况下的多传感器重构方法,并成功应用于空气质量监测网络的多传感器故障检测与隔离。所提出的方法大大减少了重建组合的数量,并允许确定故障传感器的替换值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model
This paper presents a data-driven method based on non-linear principal component analysis to detect and isolate multiple sensor faults. The RBF-NLPCA model is obtained by combining a principal curve algorithm and two three-layer radial basis function (RBF) networks. The reconstruction approach for multiple sensors is proposed in the non-linear case and successfully applied for multiple sensor fault detection and isolation of an air quality monitoring network. The proposed approach reduces considerably the number of reconstruction combinations and allows to determine replacement values for the faulty sensors.
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