Detecting Abnormal Ozone Levels using PCA-based GLR Hypothesis Testing
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abstract
Ozone is one of the lost serious air pollution problems. Monitoring abnormal changes in the concentration of ozone in the troposphere is of great interest because of its negative influence on human health, vegetation, and materials. Modeling ozone is very challenging because of the complexity of the ozone formation mechanisms in the troposphere and the uncertainty about the meteorological conditions in urban areas. In the absence of a process model, principal component analysis (PCA), which is a multivariate statistical technique, has been successfully used as a data-based fault detection (FD) method for highly correlated process variables. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abilities. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The developed PCA-based GLR FD algorithm is utilized to enhance monitoring the ozone concentrations in Upper Normandy, France. The performances of PCA and PCA-based GLR test are compared through two practical case studies, one involving a sensor fault and the other involving tropospheric ozone pollution in multiple measuring stations. The results show that the PCA-based GLR test can detect abnormal ozone levels with a smaller number of false alarms than the conventional PCA method. 2013 IEEE.
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2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)
