Abstract
Quality-related fault detection technology is an important mean to ensure safe operation and stable quality for nonlinear industrial processes, which, thus, have recently become hotspots in the process industrial control domain. In this paper, a mixed kernel partial least squares-based quality-related fault detection method is developed, which has fully considered the nonlinear characteristics of industrial processes. Under that framework, mixed kernel functions and monitoring statistics are reasonably designed, aiming at enhancing the performance of fault detection. Moreover, a case study on Tennessee Eastman process is finally given to compare with other methods to demonstrate the advantages of the new approach.
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