A new fault detection scheme based on canonical independent component analysis with application to hot strip mill process

Abstract

Aiming at the problems of inaccurate fault detection and error alarm in the process of hot strip mill process, a fault detection scheme of canonical independent component analysis is proposed. The new scheme first uses canonical variable analysis to calculate the canonical variable matrix of observation data, which effectively solves the problem of autocorrelation and cross-correlation. Then the canonical variable matrix is decomposed by independent component analysis to obtain independent elements. Finally, the data are monitored online through constructing statistics. It is proved that the accuracy of the scheme for identifying fault data is reached to 100%, and the misjudgment rate data are reduced to less than 0.6% through the simulation study of the hot strip mill process data.