A hybrid fault diagnosis strategy for chemical process startups

Abstract 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.

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