Fault Detection and Isolation of Visbreaker Unit in Oil Refinery using Multistage Gath-Geva Clustering

In this paper Fault Detection and Isolation (FDI) is shown as a pattern classification problem which can be solved using clustering techniques. Gath-Geva clustering (GGC) isexploited as optimal form by a performance assessment rule for fault detection, while multistage Gath-Geva clustering isemployed for the intent of fault isolation. Furthermore since Visbreaker unit is a large scale process, a novel hybrid method on the basis of Principle Component Analysis and GeneticAlgorithm optimization was also proposed in order to cope with the curse of dimensionality and complexity of computationproblems. There are two main percentile criteria for validation of fault detection namely specificity and sensitivity. Evaluationof fault isolation has been depicted in confusion matrix. For analysis and visualization of the correlated high dimensional data, PCA maps the data point into lower dimensional space.The proposed FDI approaches have been evaluated through experimental Visbreaker process unit data collected in oil refinery.

Keywords:

Fault Detection , Fault Isolation , Gath-Geva Clustering , Multistage Clustering , Principle Component Analysis (PCA) , Visbreaker Process Unit