Biological Nitrogen Removal Process Monitoring Based on Fuzzy Robust PCA

In this study the Fuzzy Robust Principal Component Analysis (FRPCA) method is used to monitor a biological nitrogen removal process, performances of this method are then compared with classical principal component analysis. The obtained results demonstrate the performances superiority of this robust extension compared with the conventional one. In this method fuzzy variant of PCA uses fuzzy membership and diminish the effect of outliers by assigning small membership values to outliers in order to make it robust. For the purpose of fault detection, the SPE index is used. Then the fault localization by contribution plots approach and SVI index are exploited.

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