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Enhanced data validation strategy of air quality monitoring network

机译：改进的空气质量监测网络数据验证策略

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摘要

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement).
机译：快速验证和检测已测空气质量数据中的故障是实现空气质量网络目标的关键一步。因此，本文的目标包括三个方面：（i）开发一种建模技术，该技术可用于预测空气质量变量的正常行为，并为监测目的提供准确的参考； （ii）发展故障检测方法，以有效，迅速地检测出所测空气质量数据中的任何异常。为此，将开发一种基于广义似然比测试（GLRT）和指数加权移动平均值（EWMA）组合的新故障检测方法。 GLRT是一种众所周知的统计故障检测方法，它依赖于给定错误警报率的检测概率最大化。在本文中，我们建议开发基于GLRT的EWMA故障检测方法，该方法将能够检测某些空气质量变量的值的变化。 （iii）开发故障隔离和识别方法，以定义故障源，以便适当地采取适当的纠正措施。在本文中，将开发基于中点半径主成分分析（MRPCA）模型的重建方法，以处理与空气质量监测网络相关的数据类型和模型。本文开发的所有空气质量建模，故障检测，故障隔离和重建方法都将使用真实的空气质量数据（例如颗粒物，臭氧，氮和碳氧化物测量）进行验证。

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