A hybrid fault diagnosis strategy for chemical process startups

Development of fault detection and diagnosis has been emphasized for industrial processes in order to reduce process downtimes and maintain high quality products with reduced environmental effects. Faults occur more frequently during process startups due to dramatic state variations and tendency of manual operation, and it is therefore vital to diagnose and correct any faults efficiently during process startups. In this paper, a new fault diagnosis method for process startups is developed using on-line dynamic time warping technique in combination with the principal component analysis. SymCure reasoning under the G2 Optegrity is integrated to the strategy so that the method is able to diagnose new faults unknown to historical data. The proposed method was tested on startups of a lab-scale distillation column. Results indicate that it can diagnose both known and unknown faults effectively with improved computational efficiency.

► In this paper, we present a hybrid fault detection and diagnosis strategy by integrating the dynamic locus analysis (DLA) and principal component analysis (PCA).
► By introducing the online dynamic time warping, the online process fault detection is more efficient.
► The G2 Optegrity platform is integrated into the hybrid strategy to diagnosis the faults new to the references.
► The hybrid strategy was successfully implemented to the startup of a lab-scale distillation column.