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首页> 外文期刊>Sensors Journal, IEEE >Monitoring Influent Measurements at Water Resource Recovery Facility Using Data-Driven Soft Sensor Approach
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Monitoring Influent Measurements at Water Resource Recovery Facility Using Data-Driven Soft Sensor Approach

机译：使用数据驱动的软传感器方法监测水资源回收设施的进水量

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摘要

Monitoring inflow measurements of water resource recovery facilities (WRRFs) are essential to promptly detect abnormalities and helpful in the decision making of the operators to better optimize, take corrective actions, and maintain downstream processes. In this paper, we introduced a flexible and reliable monitoring soft sensor approach to detect and identify abnormal influent measurements of WRRFs to enhance their efficiency and safety. The proposed data-driven soft sensor approach merges the desirable characteristics of principal component analysis (PCA) with$k$-nearest neighbor (KNN) scheme. PCA performed effective dimension reduction and revealed interrelationships between inflow measurements, while KNN distances demonstrated superior detection capacity, robustness to underlying data distribution, and efficiency in handling high-dimensional dataset. Furthermore, nonparametric thresholds derived from kernel density estimation further enhanced detection results of PCA-KNN approach when compared with parametric counterparts. Moreover, the radial visualization plot is innovatively employed for fault analysis and diagnosis in combination with PCA and delineated interpretable visualization of anomalies and detector performances. The effectiveness of these soft sensor schemes is evaluated by using real data from a coastal municipal WRRF located in Saudi Arabia. Also, we compared the proposed soft sensor scheme with the conventional PCA-based approaches, including standard prediction error, Hotelling’s$T^{2}$, and joint univariate methods. Results demonstrate that this soft sensor-based monitoring approach outperforms conventional PCA-based methods.
机译：监测水资源回收设施（WRRF）的流入量对于及时发现异常状况至关重要，有助于运营商做出更好的优化，采取纠正措施并维护下游流程的决策。在本文中，我们介绍了一种灵活而可靠的监控软传感器方法，用于检测和识别WRRF的异常进水量，以提高其效率和安全性。提议的数据驱动软传感器方法将主成分分析（PCA）的理想特性与 n $ k $ n近邻（KNN）方案。 PCA进行了有效的降维，并揭示了流入量之间的相互关系，而KNN距离则显示出出众的检测能力，对基础数据分布的鲁棒性以及处理高维数据集的效率。此外，与参数对应物相比，从内核密度估计得出的非参数阈值进一步增强了PCA-KNN方法的检测结果。此外，径向可视化图可与PCA结合使用，用于故障分析和诊断，可对异常情况和检测器性能进行详尽的解释性可视化。这些软传感器方案的有效性通过使用位于沙特阿拉伯的沿海市政WRRF的真实数据进行评估。此外，我们将建议的软传感器方案与基于PCA的常规方法进行了比较，包括标准预测误差，Hotelling n $ T ^ {2} $ < / inline-formula> n，以及联合单变量方法。结果表明，这种基于软传感器的监视方法优于传统的基于PCA的方法。

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  • 来源
    《Sensors Journal, IEEE》 |2019年第1期|342-352|共11页
  • 作者

    Tuoyuan Cheng; Fouzi Harrou; Ying Sun; TorOve Leiknes;

  • 作者单位

    Biological and Environmental Science and Engineering Division, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;

    Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Computer, Saudi Arabia;

    Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Computer, Saudi Arabia;

    Biological and Environmental Science and Engineering Division, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Monitoring; Principal component analysis; Sensors; Data models; Predictive models; Temperature measurement; Analytical models;

    机译：监测;主成分分析;传感器;数据模型;预测模型;温度测量;分析模型;

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