Fault Diagnosis Using Kernel Principal Component Analysis for Hot Strip Mill

In the field of hot rolling process monitoring, the activation of non-linear dynamic behaviour may render the procedure of fault diagnosis more difficult. Principal component analysis (PCA) is known as a popular method for diagnosis but as it is basically a linear method, it may pass over some useful non-linear features of the system behaviour. One possible extension of PCA is kernel PCA (KPCA), owing to the use of non-linear kernel functions that allow introduction of non-linear dependences between variables. The objective of this study is to address the problem of fault diagnosis (in terms of non-linear activation) in hot rolling automation system using a KPCA-based method. The detection is achieved by comparing the subspaces between the reference and a current state of the system through the concept of subspace angle. It is shown in this work that the exploitation of the measurements in the form of KPCA can effectively improve the detection results.