Fault detection plays an important role in identifying abnormalities in high-cost, large-scale
industrial processes. Clustering in combination with dimensionality reduction is a common
practice in data analysis and anomaly detection but is not well explored in the field of
industrial fault detection. In this paper, we apply correlation clustering before and after
dimensionality reduction to enhance fault detection on the Tennessee Eastman Process.
The reduction techniques employed are principal component analysis (PCA) and dynamic …